{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1c72f79-5f18-43a8-a309-2c71b1f21745",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device cuda!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy.random import shuffle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import wfdb\n",
    "from pathlib import Path\n",
    "import math\n",
    "from math import floor\n",
    "import cv2\n",
    "import pywt\n",
    "from functools import partial\n",
    "from collections import defaultdict\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from numba import njit\n",
    "from tqdm import tqdm\n",
    "from time import time\n",
    "import tsfresh\n",
    "import tsfel\n",
    "import pickle as pkl\n",
    "import pandas as pd\n",
    "import traceback\n",
    "import sys\n",
    "import gc\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, make_scorer, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import balanced_accuracy_score, f1_score, precision_score, recall_score\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE, Isomap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler, Normalizer, MinMaxScaler\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import scipy\n",
    "from scipy import signal\n",
    "from scipy.stats import kendalltau\n",
    "\n",
    "import torch\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torch.backends import cudnn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import skorch\n",
    "from skorch.helper import predefined_split\n",
    "from skorch.callbacks import EpochScoring, Initializer, LRScheduler, TensorBoard, Checkpoint, Initializer\n",
    "from skorch.dataset import Dataset\n",
    "\n",
    "cudnn.benchmark = False\n",
    "cudnn.deterministic = True\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "print(f\"Using device {DEVICE}!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd52c8d3-7707-4624-8736-4646e5dae05e",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a530e645-e1ad-4e82-9a39-57c53f61c432",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_mapping = {\n",
    "    \"N\": 0, \"L\": 0, \"R\": 0, \"e\": 0, \"j\": 0,  # N\n",
    "    \"A\": 1, \"a\": 1, \"S\": 1, \"J\": 1,  # SVEB\n",
    "    \"V\": 2, \"E\": 2,  # VEB\n",
    "    \"F\": 3,  # F\n",
    "    \"/\": 4, \"f\": 4, \"Q\": 4, \"x\": 4, \"!\": 4  # Q\n",
    "}\n",
    "\n",
    "@njit(parallel=True)\n",
    "def njit_dtw(s1, s2):\n",
    "    mat_d = np.ones((len(s1) + 1, len(s2) + 1)) * np.Inf\n",
    "    mat_d[0, 0] = 0\n",
    "    for i in range(1, len(s1) + 1):\n",
    "        for j in range(1, len(s2) + 1):\n",
    "            d = (s1[i - 1] - s2[j - 1])**2\n",
    "            mat_d[i, j] = d + min((mat_d[i-1, j], mat_d[i, j-1], mat_d[i-1, j-1]))\n",
    "    return np.sqrt(mat_d[len(s1), len(s2)])\n",
    "\n",
    "\n",
    "def read_ecg_data(path, record, lead, final_sampling_rate=None, cwt_freqs=100):\n",
    "    data = wfdb.rdsamp(str(path / str(record)))\n",
    "    annotations = wfdb.rdann(str(path / str(record)), extension='atr')\n",
    "    \n",
    "    sampling_rate = data[1]['fs']\n",
    "    try:\n",
    "        lead_id = data[1]['sig_name'].index(lead)\n",
    "    except ValueError:\n",
    "        return None\n",
    "    \n",
    "    X = data[0][:, lead_id]\n",
    "\n",
    "    medfilt_sr_1 = int(0.2 * sampling_rate)\n",
    "    medfilt_sr_1 += 1 if medfilt_sr_1 % 2 == 0 else 0\n",
    "    medfilt_sr_2 = int(0.6 * sampling_rate)\n",
    "    medfilt_sr_2 += 1 if medfilt_sr_2 % 2 == 0 else 0\n",
    "    baseline = signal.medfilt(signal.medfilt(X, medfilt_sr_1), medfilt_sr_2)\n",
    "    X -= baseline\n",
    "    \n",
    "    y = np.asarray(annotations.symbol)\n",
    "    loc = np.asarray(annotations.sample)\n",
    "\n",
    "    if final_sampling_rate is not None and final_sampling_rate != sampling_rate:\n",
    "        sampling_ratio = final_sampling_rate / sampling_rate\n",
    "        X = scipy.signal.resample(X, int(X.shape[0] * sampling_ratio))\n",
    "        loc = np.floor(loc * sampling_ratio).astype(np.int64)\n",
    "\n",
    "    indices = [i for i, label in enumerate(y) if label in label_mapping.keys()]\n",
    "    y, loc = y[indices], loc[indices]\n",
    "\n",
    "    X = X / np.mean(X[loc])\n",
    "\n",
    "    y = np.asarray([label_mapping[l] for l in y])\n",
    "    \n",
    "    scales = pywt.central_frequency('mexh') * sampling_rate / np.arange(1, cwt_freqs + 1, 1)\n",
    "    X_cwt, _ = pywt.cwt(X, scales, 'mexh', 1 / sampling_rate)\n",
    "    return X, X_cwt, np.asarray(y), np.asarray(loc)\n",
    "\n",
    "\n",
    "def get_rr_data(loc):\n",
    "    loc = loc.astype(np.float32)\n",
    "    avg_rr = np.mean(np.diff(loc))\n",
    "    prev_rr = []\n",
    "    next_rr = []\n",
    "    ratio_rr = []\n",
    "    local_rr = []\n",
    "    for i in range(1, len(loc) - 1):\n",
    "            prev_rr.append(loc[i] - loc[i - 1] - avg_rr)\n",
    "            next_rr.append(loc[i + 1] - loc[i] - avg_rr)\n",
    "            ratio_rr.append((loc[i] - loc[i - 1]) / (loc[i + 1] - loc[i]))\n",
    "            local_rr.append(np.mean(np.diff(loc[np.maximum(i - 10, 0):i + 1])) - avg_rr)\n",
    "    return np.asarray(prev_rr), np.asarray(next_rr), np.asarray(local_rr), np.asarray(ratio_rr)\n",
    "\n",
    "\n",
    "def load_dataset(path, record_ids, lead='MLII', sampling_rate=360, n_before=90, n_after=110, cwt_freqs=100):\n",
    "    X_windows = []\n",
    "    X_cwt_windows = []\n",
    "    rr_windows = []\n",
    "    y_windows = []\n",
    "\n",
    "    n_before = int(n_before * (sampling_rate / 360))\n",
    "    n_after = int(n_after * (sampling_rate / 360))\n",
    "    n_total = n_before + n_after\n",
    "    \n",
    "    for record in record_ids:\n",
    "        X, X_cwt, y, loc = read_ecg_data(path, record, lead, final_sampling_rate=sampling_rate, cwt_freqs=cwt_freqs)\n",
    "        prev_rr, next_rr, local_rr, ratio_rr = get_rr_data(loc)\n",
    "        y = y[1:-1]\n",
    "        loc = loc[1:-1]\n",
    "        \n",
    "        for i, (rpeak, label) in enumerate(zip(loc, y)):\n",
    "                window = X[rpeak - n_before:rpeak + n_after]\n",
    "\n",
    "                if len(window) == n_total:\n",
    "                    X_windows.append(window)\n",
    "                    X_cwt_windows.append(cv2.resize(X_cwt[:, rpeak - n_before:rpeak + n_after], (n_total // 2, cwt_freqs)))\n",
    "                    rr_windows.append([prev_rr[i], next_rr[i], local_rr[i], ratio_rr[i]])\n",
    "                    y_windows.append([label, record])\n",
    "            \n",
    "    return np.asarray(X_windows), np.asarray(X_cwt_windows), np.asarray(rr_windows), np.asarray(y_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d424376f-398b-4d80-8ba5-ca4d4b917f35",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## MIT-BIH supraventricular processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d96e807d-cd62-412b-ada9-1967046f272a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('data/mit-bih-supraventricular-arrhythmia-database-1.0.0/')\n",
    "\n",
    "label_mapping = {\n",
    "    'N': 'N',\n",
    "    'L': 'N',\n",
    "    'R': 'N',\n",
    "    'B': 'N',\n",
    "    'A': 'S',\n",
    "    'a': 'S',\n",
    "    'x': 'S',\n",
    "    'J': 'S',\n",
    "    'V': 'V',\n",
    "    'F': 'F',\n",
    "    '!': 'V',\n",
    "    'e': 'N',\n",
    "    'j': 'N',\n",
    "    'E': 'V',\n",
    "#     'f': 'Q',\n",
    "#     'Q': 'Q',\n",
    "}\n",
    "\n",
    "label_categories = set(label_mapping.values())\n",
    "\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "sampling_rate = 128\n",
    "lead_id = 'ECG2'\n",
    "\n",
    "patient_ids = list(set([f.stem for f in data_root.glob('*') if f.stem.isnumeric()]))\n",
    "training_ids, testing_ids = train_test_split(patient_ids, train_size=.5, random_state=755)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2aac1b3a-597a-4bc9-a62b-b58d1e4f0a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((89606, 71), (89606, 71), (89606, 100, 100), (89606, 4), (89606, 2))"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_local, X_cwt, rr_information, y = load_dataset(data_root, training_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "np.save(Path('processed_data/mit-bih-supra/X_train'), X)\n",
    "np.save(Path('processed_data/mit-bih-supra/X_local_train'), X_local)\n",
    "np.save(Path('processed_data/mit-bih-supra/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih-supra/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih-supra/y_train'), y)\n",
    "\n",
    "X.shape, X_local.shape, X_cwt.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4634ecbd-69fc-4130-9660-40391a647ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((93817, 71), (93817, 71), (93817, 100, 100), (93817, 4), (93817, 2))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_local, X_cwt, rr_information, y = load_dataset(data_root, testing_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "\n",
    "np.save(Path('processed_data/mit-bih-supra/X_test'), X)\n",
    "np.save(Path('processed_data/mit-bih-supra/X_local_test'), X_local)\n",
    "np.save(Path('processed_data/mit-bih-supra/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih-supra/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih-supra/y_test'), y)\n",
    "\n",
    "X.shape, X_local.shape, X_cwt.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e9c2de-17ca-462e-bcdc-3e801a4c77d9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## INCART processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "eb60efc5-40b1-43da-b97e-ab847d83e2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('data/st-petersburg-incart-12-lead-arrhythmia-database-1.0.0')\n",
    "\n",
    "patient_mapping = {\n",
    "    1: ['I01', 'I02'],\n",
    "    2: ['I03', 'I04', 'I05'],\n",
    "    3: ['I06', 'I07'],\n",
    "    4: ['I08'],\n",
    "    5: ['I09', 'I10', 'I11'],\n",
    "    6: ['I12', 'I13', 'I14'],\n",
    "    7: ['I15'],\n",
    "    8: ['I16', 'I17'],\n",
    "    9: ['I18', 'I19'],\n",
    "    10: ['I20', 'I21', 'I22'],\n",
    "    11: ['I23', 'I24'],\n",
    "    12: ['I25', 'I26'],\n",
    "    13: ['I27', 'I28'],\n",
    "    14: ['I29', 'I30', 'I31', 'I32'],\n",
    "    15: ['I33', 'I34'],\n",
    "    16: ['I35', 'I36', 'I37'],\n",
    "    17: ['I38', 'I39'],\n",
    "    18: ['I40', 'I41'],\n",
    "    19: ['I42', 'I43'],\n",
    "    20: ['I44', 'I45', 'I46'],\n",
    "    21: ['I47', 'I48'],\n",
    "    22: ['I49', 'I50'],\n",
    "    23: ['I51', 'I52', 'I53'],\n",
    "    24: ['I54', 'I55', 'I56'],\n",
    "    25: ['I57', 'I58'],\n",
    "    26: ['I59', 'I60', 'I61'],\n",
    "    27: ['I62', 'I63', 'I64'],\n",
    "    28: ['I65', 'I66', 'I67'],\n",
    "    29: ['I68', 'I69'],\n",
    "    30: ['I70', 'I71'],\n",
    "    31: ['I72', 'I73'],\n",
    "    32: ['I74', 'I75']\n",
    "}\n",
    "\n",
    "label_mapping = {\n",
    "    'N': 'N',\n",
    "    'V': 'V',\n",
    "    'A': 'S',\n",
    "    'j': 'N',\n",
    "    'F': 'F',\n",
    "    'S': 'S',\n",
    "    'n': 'S',\n",
    "    'B': 'N',\n",
    "    'R': 'N',\n",
    "    'Q': 'Q'\n",
    "}\n",
    "\n",
    "label_categories = set(label_mapping.values())\n",
    "\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "sampling_rate = 257\n",
    "lead_id = 'II'\n",
    "\n",
    "training_patients, testing_patients = train_test_split(list(patient_mapping.keys()), train_size=.5, random_state=755)\n",
    "training_ids = np.concatenate([[f for f in patient_mapping[p]] for p in training_patients]).tolist()\n",
    "testing_ids = np.concatenate([[f for f in patient_mapping[p]] for p in testing_patients]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f257dcd8-7d73-4569-b4e2-22eef27a97dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, training_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_train'), X)\n",
    "np.save(Path('processed_data/incart/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/incart/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/incart/y_train'), y)\n",
    "\n",
    "X.shape, X_cwt.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f2f7fe9-8701-487f-a5cf-66baad161bb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Feature extraction started ***\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "              <p>\n",
       "                  Progress: 66% Complete\n",
       "              <p/>\n",
       "              <progress\n",
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       "                  max='94221',\n",
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.load(Path('processed_data/incart/X_train.npy'))\n",
    "y = np.load(Path('processed_data/incart/y_train.npy'))\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/incart/X_feat_train.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f1f89217-7168-45e2-82c8-aa4bd7b211f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/incart/X_feat_train.csv'))\n",
    "y = np.load(Path('processed_data/incart/y_train.npy'))[:, 0]\n",
    "\n",
    "tau_vals = sorted([(feat, (result.pvalue, -abs(result.statistic))) for feat, result in [(feat, kendalltau(X_feat[feat], y)) for feat in X_feat.columns]], key=lambda r: r[1])\n",
    "sorted_features = [feat for feat, _ in tau_vals]\n",
    "X_feat = X_feat[sorted_features].values\n",
    "sorted_features = np.asarray(sorted_features)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_feat_train'), X_feat)\n",
    "np.save(Path('processed_data/incart/sorted_features'), sorted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "3be15a13-b704-4d48-af01-6ae6320ff58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((81503, 142), (81503, 100, 71), (81503, 4), (81503, 2))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, testing_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_test'), X)\n",
    "np.save(Path('processed_data/incart/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/incart/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/incart/y_test'), y)\n",
    "\n",
    "X.shape, X_cwt.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "150089c7-3eb8-4416-aa9d-485d79c3e63c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Feature extraction started ***\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "              <p>\n",
       "                  Progress: 70% Complete\n",
       "              <p/>\n",
       "              <progress\n",
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     "metadata": {},
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   ],
   "source": [
    "X = np.load(Path('processed_data/incart/X_test.npy'))\n",
    "y = np.load(Path('processed_data/incart/y_test.npy'))\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/incart/X_feat_test.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9da83100-254b-45fb-962b-a88aae0f5e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/incart/X_feat_test.csv'))\n",
    "sorted_features = np.load(Path('processed_data/incart/sorted_features.npy')).tolist()\n",
    "\n",
    "X_feat = X_feat[sorted_features].values\n",
    "\n",
    "np.save(Path('processed_data/incart/X_feat_test'), X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59986de4-19c8-46d4-8c97-7956cf8cd96e",
   "metadata": {},
   "source": [
    "## MIT BIH processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b2af0f60-1b76-4e0d-a587-ce3c57eb0a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('/data/IDLab/DigiHealth/mit-bih-arrhythmia-database-1.0.0/')\n",
    "\n",
    "training_ids = [\n",
    "    101, 106, 108, 109, 112,\n",
    "    114, 115, 116, 118, 119,\n",
    "    122, 124, 201, 203, 205,\n",
    "    207, 208, 209, 215, 220,\n",
    "    223, 230\n",
    "]\n",
    "testing_ids = [\n",
    "    100, 103, 105, 111, 113,\n",
    "    117, 121, 123, 200, 202,\n",
    "    210, 212, 213, 214, 219,\n",
    "    221, 222, 228, 231, 232,\n",
    "    233, 234\n",
    "]\n",
    "\n",
    "sampling_rate = 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff565a6f-ccd7-4f01-9da6-f5323f640390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Feature extraction started ***\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "              <p>\n",
       "                  Progress: 100% Complete\n",
       "              <p/>\n",
       "              <progress\n",
       "                  value='51507'\n",
       "                  max='51507',\n",
       "                  style='width: 25%',\n",
       "              >\n",
       "                  51507\n",
       "              </progress>\n",
       "\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Feature extraction finished ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((51507, 142), (51507, 100, 71), (51507, 4), (51507, 205), (51507, 2))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, training_ids, sampling_rate=257)\n",
    "np.save(Path('processed_data/mit-bih/X_train'), X)\n",
    "np.save(Path('processed_data/mit-bih/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih/y_train'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/mit-bih/X_feat_train.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, rr_information.shape, X_feat.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "94fe5ff7-044a-417a-92ba-30b2468cfff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-bih/X_feat_train.csv'))\n",
    "y = np.load(Path('processed_data/mit-bih/y_train.npy'))[:, 0]\n",
    "\n",
    "tau_vals = sorted([(feat, (result.pvalue, -abs(result.statistic))) for feat, result in [(feat, kendalltau(X_feat[feat], y)) for feat in X_feat.columns]], key=lambda r: r[1])\n",
    "sorted_features = [feat for feat, _ in tau_vals]\n",
    "X_feat = X_feat[sorted_features].values\n",
    "sorted_features = np.asarray(sorted_features)\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_feat_train'), X_feat)\n",
    "np.save(Path('processed_data/mit-bih/sorted_features'), sorted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "27ad92d8-68b3-40f7-bcde-350b2d71b9a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Feature extraction started ***\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "              <p>\n",
       "                  Progress: 100% Complete\n",
       "              <p/>\n",
       "              <progress\n",
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       "                  max='49803',\n",
       "                  style='width: 25%',\n",
       "              >\n",
       "                  49803\n",
       "              </progress>\n",
       "\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Feature extraction finished ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((49803, 142), (49803, 100, 71), (49803, 4), (49803, 205), (49803, 2))"
      ]
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     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, testing_ids, sampling_rate=257)\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_test'), X)\n",
    "np.save(Path('processed_data/mit-bih/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih/y_test'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/mit-bih/X_feat_test.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, rr_information.shape, X_feat.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c0cf97f6-f24e-4f30-9b4b-01e4aef3e464",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-bih/X_feat_test.csv'))\n",
    "sorted_features = np.load(Path('processed_data/mit-bih/sorted_features.npy')).tolist()\n",
    "\n",
    "X_feat = X_feat[sorted_features].values\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_feat_test'), X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb6fb04-adbe-4179-b7be-4d6b333e6bba",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Combine datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8915a704-290c-4273-a81e-7455f0f178ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_dataset(dataset_id, new_size, new_sampling_rate):\n",
    "    X_train = np.load(Path(f\"processed_data/{dataset_id}/X_train.npy\"))\n",
    "    rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\"))\n",
    "    y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))\n",
    "    \n",
    "    X_test = np.load(Path(f\"processed_data/{dataset_id}/X_test.npy\"))\n",
    "    rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\"))\n",
    "    y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))\n",
    "\n",
    "    print(X_train.shape)\n",
    "    X_train = scipy.signal.resample(X_train, new_size, axis=-1)\n",
    "    print(X_train.shape)\n",
    "    X_test = scipy.signal.resample(X_test, new_size, axis=-1)\n",
    "\n",
    "    new_dataset_id = dataset_id + f\"-{new_sampling_rate}Hz\"\n",
    "    dataset_path = Path('processed_data') / new_dataset_id\n",
    "    dataset_path.mkdir(exist_ok=True)\n",
    "    \n",
    "    np.save(dataset_path / 'X_train', X_train)\n",
    "    np.save(dataset_path / 'rr_train', rr_train)\n",
    "    np.save(dataset_path / 'y_train', y_train)\n",
    "    \n",
    "    np.save(dataset_path / 'X_test', X_test)\n",
    "    np.save(dataset_path / 'rr_test', rr_test)\n",
    "    np.save(dataset_path / 'y_test', y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4623914-261a-4d1c-9b3d-b88f6153def9",
   "metadata": {},
   "source": [
    "### Resample datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3f3cadc-77f6-438d-a50e-7b6316e2f173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93714, 142)\n",
      "(93714, 71)\n"
     ]
    }
   ],
   "source": [
    "resample_dataset('incar', new_size=71, new_sampling_rate=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a662dc8-b54c-40f7-a37b-e56da60d44b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(52272, 200)\n",
      "(52272, 71)\n"
     ]
    }
   ],
   "source": [
    "resample_dataset('mit-bih', new_size=71, new_sampling_rate=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a635a1-6a95-414a-a747-5628afd8bdf0",
   "metadata": {},
   "source": [
    "### Combine datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e68bb25-13ef-40ba-8859-6d36979d9e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((233983, 71),\n",
       " (224023, 71),\n",
       " (array(['N', 'S', 'V'], dtype='<U1'), array([207047,   6215,  20721])),\n",
       " (array(['N', 'S', 'V'], dtype='<U1'), array([196634,  10870,  16519])))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = [Path('processed_data') / ds for ds in ['incar-128Hz', 'mit-bih-128Hz', 'mit-bih-supra']]\n",
    "\n",
    "label_mapping = {\n",
    "    'N': 'N',\n",
    "    'L': 'N',\n",
    "    'R': 'N',\n",
    "    'B': 'N',\n",
    "    'A': 'S',\n",
    "    'a': 'S',\n",
    "    'x': 'S',\n",
    "    'J': 'S',\n",
    "    'V': 'V',\n",
    "    #'F': 'F',\n",
    "    '!': 'V',\n",
    "    'e': 'N',\n",
    "    'j': 'N',\n",
    "    'E': 'V',\n",
    "    'S': 'S',\n",
    "    'n': 'S',\n",
    "    'J': 'S',\n",
    "}\n",
    "label_categories = set(label_mapping.values())\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "X_train = []\n",
    "rr_train = []\n",
    "y_train = []\n",
    "X_test = []\n",
    "rr_test = []\n",
    "y_test = []\n",
    "for ds_path in datasets:\n",
    "    X_train.append(np.load(ds_path / 'X_train.npy'))\n",
    "    rr_train.append(np.load(ds_path / 'rr_train.npy'))\n",
    "    y_train.append(np.load(ds_path / 'y_train.npy'))\n",
    "\n",
    "    X_test.append(np.load(ds_path / 'X_test.npy'))\n",
    "    rr_test.append(np.load(ds_path / 'rr_test.npy'))\n",
    "    y_test.append(np.load(ds_path / 'y_test.npy'))\n",
    "\n",
    "X_train = np.concatenate(X_train)\n",
    "rr_train = np.concatenate(rr_train)\n",
    "y_train = np.concatenate(y_train)\n",
    "\n",
    "X_test = np.concatenate(X_test)\n",
    "rr_test = np.concatenate(rr_test)\n",
    "y_test = np.concatenate(y_test)\n",
    "\n",
    "X_train = np.asarray([w for w, l in zip(X_train, y_train) if l[0] in label_mapping]).astype(np.float32)\n",
    "rr_train = np.asarray([w for w, l in zip(rr_train, y_train) if l[0] in label_mapping]).astype(np.float32)\n",
    "y_train = y_train[:, 0]\n",
    "y_train = np.asarray([label_mapping[l] for l in y_train if l in label_mapping])\n",
    "\n",
    "X_test = np.asarray([w for w, l in zip(X_test, y_test) if l[0] in label_mapping]).astype(np.float32)\n",
    "rr_test = np.asarray([w for w, l in zip(rr_test, y_test) if l[0] in label_mapping]).astype(np.float32)\n",
    "y_test = y_test[:, 0]\n",
    "y_test = np.asarray([label_mapping[l] for l in y_test if l in label_mapping])\n",
    "\n",
    "X_train.shape, X_test.shape, np.unique(y_train, return_counts=True), np.unique(y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c76ddef-b75a-4a11-ab20-10b0fc3a9084",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(Path('processed_data/combined/X_train'), X_train)\n",
    "np.save(Path('processed_data/combined/rr_train'), rr_train)\n",
    "np.save(Path('processed_data/combined/y_train'), y_train)\n",
    "\n",
    "np.save(Path('processed_data/combined/X_test'), X_test)\n",
    "np.save(Path('processed_data/combined/rr_test'), rr_test)\n",
    "np.save(Path('processed_data/combined/y_test'), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032d2246-e41f-4b57-b732-5e8c9b278e99",
   "metadata": {},
   "source": [
    "# Supervised learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf9d521-e395-4bf1-b770-d16d950a8ba9",
   "metadata": {},
   "source": [
    "## Baseset load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2b993422-61e6-452b-9ba6-fa85ecd95beb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50555, 100, 71), (50555,), (49273, 100, 71), (49273,))"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_id = 'mit-bih'\n",
    "\n",
    "num_to_label = {0: 'N', 1: 'S', 2: 'V', 3: 'F', 4: 'Q'}\n",
    "label_mapping = {v: k for k, v in num_to_label.items()}\n",
    "allowed_labels = [0, 2, 1]\n",
    "\n",
    "X_cwt_train = np.load(Path(f\"processed_data/{dataset_id}/X_cwt_train.npy\")).astype(np.float32)\n",
    "X_feat_train = np.load(Path(f\"processed_data/{dataset_id}/X_feat_train.npy\")).astype(np.float32)\n",
    "rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\")).astype(np.float32)\n",
    "y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))[:, 0].astype(np.int64)\n",
    "#y_train = np.asarray([label_mapping[l] for l in y_train]).astype(np.int64)\n",
    "\n",
    "X_cwt_test = np.load(Path(f\"processed_data/{dataset_id}/X_cwt_test.npy\")).astype(np.float32)\n",
    "X_feat_test = np.load(Path(f\"processed_data/{dataset_id}/X_feat_test.npy\")).astype(np.float32)\n",
    "rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\")).astype(np.float32)\n",
    "y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))[:, 0].astype(np.int64)\n",
    "#y_test = np.asarray([label_mapping[l] for l in y_test]).astype(np.int64)\n",
    "\n",
    "X_cwt_train_filtered = np.asarray([w for w, l in zip(X_cwt_train, y_train) if l in allowed_labels])\n",
    "X_feat_train_filtered = np.asarray([w for w, l in zip(X_feat_train, y_train) if l in allowed_labels])\n",
    "rr_train_filtered = np.asarray([w for w, l in zip(rr_train, y_train) if l in allowed_labels])\n",
    "y_train_filtered = np.asarray([l for l in y_train if l in allowed_labels]).astype(np.int64)\n",
    "\n",
    "del X_cwt_train, X_feat_train, rr_train, y_train\n",
    "gc.collect()\n",
    "X_cwt_train = X_cwt_train_filtered\n",
    "X_feat_train = X_feat_train_filtered\n",
    "rr_train = rr_train_filtered\n",
    "y_train = y_train_filtered\n",
    "\n",
    "X_cwt_test_filtered = np.asarray([w for w, l in zip(X_cwt_test, y_test) if l in allowed_labels])\n",
    "X_feat_test_filtered = np.asarray([w for w, l in zip(X_feat_test, y_test) if l in allowed_labels])\n",
    "rr_test_filtered = np.asarray([w for w, l in zip(rr_test, y_test) if l in allowed_labels])\n",
    "y_test_filtered = np.asarray([l for l in y_test if l in allowed_labels]).astype(np.int64)\n",
    "\n",
    "del X_cwt_test, X_feat_test, rr_test, y_test\n",
    "gc.collect()\n",
    "X_cwt_test = X_cwt_test_filtered\n",
    "X_feat_test = X_feat_test_filtered\n",
    "rr_test = rr_test_filtered\n",
    "y_test = y_test_filtered\n",
    "\n",
    "rr_scaler = RobustScaler()\n",
    "rr_train = rr_scaler.fit_transform(rr_train)\n",
    "rr_test = rr_scaler.transform(rr_test)\n",
    "\n",
    "feat_scaler = RobustScaler()\n",
    "X_feat_train = feat_scaler.fit_transform(X_feat_train)\n",
    "X_feat_test = feat_scaler.transform(X_feat_test)\n",
    "\n",
    "X_cwt_train.shape, y_train.shape, X_cwt_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5bcd46f0-51d3-4abb-8e49-aed6fa1582a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cwt_train = np.concatenate([X_cwt_train, X_cwt_incart])\n",
    "X_feat_train = np.concatenate([X_feat_train, X_feat_incart])\n",
    "rr_train = np.concatenate([rr_train, rr_incart])\n",
    "y_train = np.concatenate([y_train, y_incart])\n",
    "\n",
    "rr_scaler = RobustScaler()\n",
    "rr_train = rr_scaler.fit_transform(rr_train)\n",
    "rr_test = rr_scaler.transform(rr_test)\n",
    "\n",
    "feat_scaler = RobustScaler()\n",
    "X_feat_train = feat_scaler.fit_transform(X_feat_train)\n",
    "X_feat_test = feat_scaler.transform(X_feat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dabf9868-416b-4783-9a74-0da956eab07c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), array([153542,   1990,  20000]))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cwt_incart = np.concatenate([X_cwt_train, X_cwt_test])\n",
    "X_feat_incart = np.concatenate([X_feat_train, X_feat_test])\n",
    "rr_incart = np.concatenate([rr_train, rr_test])\n",
    "y_incart = np.concatenate([y_train, y_test])\n",
    "np.unique(y_incart, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a9d0e2-cd1f-4320-915a-6ff30f285965",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Full load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb106e64-44ee-4f0a-a727-a54de47cf067",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_id = 'combined'\n",
    "\n",
    "label_to_num = {'N': 0, 'S': 1, 'V': 2}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "X_train = np.load(Path(f\"processed_data/{dataset_id}/X_train.npy\"))\n",
    "rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\"))\n",
    "y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))\n",
    "y_train = np.asarray([label_to_num[l] for l in y_train]).astype(np.int64)\n",
    "\n",
    "X_test = np.load(Path(f\"processed_data/{dataset_id}/X_test.npy\"))\n",
    "rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\"))\n",
    "y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))\n",
    "y_test = np.asarray([label_to_num[l] for l in y_test]).astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4472aaf-a865-4783-b135-ec30f1dadc60",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = RandomUnderSampler(random_state=7665)\n",
    "X_train, _ = sampler.fit_resample(X_train, y_train)\n",
    "rr_train, y_train = sampler.fit_resample(rr_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac164a9-8c33-4055-88b2-955eba62b969",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9107a6ad-7ce8-4ddf-b440-8d8377fe4596",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2342/2342927971.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_counts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_counts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "train_labels, train_counts = np.unique(y_train[y_train[:, 0] == 'F'][:, 1], return_counts=True)\n",
    "test_labels, test_counts = np.unique(y_test[y_test[:, 0] == 'F'][:, 1], return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.bar(np.arange(len(train_labels) + len(test_labels)), np.concatenate([train_counts, test_counts]))\n",
    "plt.xticks(np.arange(len(train_labels) + len(test_labels)), np.concatenate([train_labels, test_labels]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1ff409d4-fd2c-45d8-8df5-464656712e8b",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2342/339340707.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_beats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'208'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtesting_beats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'213'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "training_beats = X_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_beats = X_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(20, 10), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].plot(training_beats.T, color='b', alpha=.01)\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].plot(testing_beats.T, color='orange', alpha=.01)\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cd10242d-7ac8-4840-a4b3-473941a9aa2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_local_beats = X_local_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_local_beats = X_local_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(20, 10), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].plot(training_local_beats.T, color='b', alpha=.01)\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].plot(testing_local_beats.T, color='orange', alpha=.01)\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8072a5eb-019f-47b0-8ccc-a8bd2c4ff76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_rr = rr_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_rr = rr_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "training_pca = pca.fit_transform(training_rr)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "testing_pca = pca.fit_transform(testing_rr)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].scatter(training_pca[:, 0], training_pca[:, 1], color='b')\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].scatter(testing_pca[:, 0], testing_pca[:, 1], color='orange')\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ad8f47-3ef7-4f59-9d03-3e0047244f8f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "efbdbdb3-93ee-411a-9aab-b842297418f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=123)\n",
    "X_train_resampled, y_train_resampled = sm.fit_resample(X_train, y_train)\n",
    "sm = SMOTE(random_state=123)\n",
    "X_local_train_resampled, _ = sm.fit_resample(X_local_train, y_train)\n",
    "sm = SMOTE(random_state=123)\n",
    "rr_train_resampled, _ = sm.fit_resample(rr_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5002b04-490c-4fba-9632-a44ca72e2258",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_patients = np.unique(y_train[:, 1])\n",
    "X_train_resampled = []\n",
    "X_local_train_resampled = []\n",
    "rr_train_resampled = []\n",
    "y_train_resampled = []\n",
    "for patient_id in training_patients:\n",
    "    indices = y_train[:, 1] == patient_id\n",
    "    X, X_local, rr, y = X_train[indices], X_local_train[indices], rr_train[indices], y_train[indices, 0]\n",
    "    \n",
    "    if np.unique(y).shape[0] > 1:\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        X, _ = resampler.fit_resample(X, y)\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        X_local, _ = resampler.fit_resample(X_local, y)\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        rr, y = resampler.fit_resample(rr, y)\n",
    "        \n",
    "        X_train_resampled.append(X)\n",
    "        X_local_train_resampled.append(X_local)\n",
    "        rr_train_resampled.append(rr)\n",
    "        y_train_resampled.append(y)\n",
    "        \n",
    "X_train_resampled = np.concatenate(X_train_resampled)\n",
    "X_local_train_resampled = np.concatenate(X_local_train_resampled)\n",
    "rr_train_resampled = np.concatenate(rr_train_resampled)\n",
    "y_train_resampled = np.concatenate(y_train_resampled).astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0be2d93-bded-4f52-af4a-e12514c234c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "std = .00\n",
    "\n",
    "X_train = X_train_resampled + std * np.random.randn(*X_train_resampled.shape).astype(np.float32)\n",
    "X_local_train = X_local_train_resampled + std * np.random.randn(*X_local_train_resampled.shape).astype(np.float32)\n",
    "rr_train = rr_train_resampled + std * np.random.randn(*rr_train_resampled.shape).astype(np.float32)\n",
    "y_train = y_train_resampled.astype(np.int64)\n",
    "#y_test = y_test[:, 0].astype(np.float64).astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e7a8af-e07f-404c-820a-207a9df23f8a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "63aa8cee-a080-4626-91aa-155d2ae28d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN2D(torch.nn.Module):\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "    \n",
    "    def __init__(self, n_features=0, n_lt_features=0, lrelu_slope=.2):\n",
    "        super(CNN2D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.lrelu_slope = lrelu_slope\n",
    "        self.n_features = n_features\n",
    "        self.n_lt_features = n_lt_features\n",
    "        self.feature_head_output = 64 if (n_features != 0 or n_lt_features != 0) else 4\n",
    "        self.beat_head = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=1, out_channels=16, kernel_size=7),\n",
    "            torch.nn.BatchNorm2d(16),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.MaxPool2d(5),\n",
    "            \n",
    "            torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(32),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.MaxPool2d(3),\n",
    "            \n",
    "            torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.AdaptiveMaxPool2d((1, 1)),\n",
    "            \n",
    "            torch.nn.Flatten()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.feature_head = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features + self.n_lt_features, 128),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(128, 128),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(128, self.feature_head_output),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "        ).to(DEVICE)\n",
    "        \n",
    "        self.linear_blocks = torch.nn.Sequential(\n",
    "            torch.nn.Linear(64 + self.feature_head_output, 256),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(256, 64),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(64, 32),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(32, 3))\n",
    "        \n",
    "        self.beat_head.apply(CNN2D.__init__weights)\n",
    "        self.feature_head.apply(CNN2D.__init__weights)\n",
    "        self.linear_blocks.apply(CNN2D.__init__weights)\n",
    "\n",
    "\n",
    "    def forward(self, X, X_rr, X_feat=None, X_lt_feat=None):\n",
    "        X = torch.unsqueeze(X, 1)\n",
    "        if X_feat is not None:\n",
    "            X_feat = torch.cat((X_rr, X_feat), dim=1)\n",
    "        if X_lt_feat is not None:\n",
    "            if X_feat is not None:\n",
    "                X_feat = torch.cat((X_feat, X_lt_feat), dim=1)\n",
    "            else:\n",
    "                X_feat = torch.cat((X_rr, X_lt_feat), dim=1)\n",
    "        \n",
    "        z_beat = self.beat_head(X)\n",
    "        if X_feat is not None:\n",
    "            z_feat = self.feature_head(X_feat)\n",
    "        else:\n",
    "            z_feat = X_rr\n",
    "        \n",
    "        z = torch.cat((z_beat, z_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class CNNDeep2D(torch.nn.Module):\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "\n",
    "    def __init__(self, n_features=0):\n",
    "        super(CNNDeep2D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.n_features = n_features\n",
    "        self.feature_head_output = 32 if n_features != 0 else 4\n",
    "\n",
    "        self.conv_block_1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=5, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_1.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_2.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_3 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(4),\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_3.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_4.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_5 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(5),\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_5.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_6 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_6.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.flatten = torch.nn.Sequential(\n",
    "            torch.nn.AdaptiveMaxPool2d((1, 1)),\n",
    "            torch.nn.Flatten()\n",
    "        )\n",
    "\n",
    "        self.feature_head = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 32),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.feature_head.apply(CNNDeep2D.__init__weights)\n",
    "        \n",
    "        self.linear_blocks = torch.nn.Sequential(\n",
    "            torch.nn.Linear(256 + self.feature_head_output, 256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(256, 32),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(32, self.n_classes)\n",
    "        ).to(DEVICE)\n",
    "        self.linear_blocks.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "    \n",
    "    def forward(self, X, X_rr, X_feat=None):\n",
    "        if X_feat is not None:\n",
    "            X_feat = torch.cat((X_rr, X_feat), dim=1)\n",
    "        \n",
    "        z_1 = self.conv_block_1(X)\n",
    "        z_2 = F.relu(z_1 + self.conv_block_2(z_1))\n",
    "        z_3 = self.conv_block_3(z_2)\n",
    "        z_4 = F.relu(z_3 + self.conv_block_4(z_3))\n",
    "        z_5 = self.conv_block_5(z_4)\n",
    "        z_6 = F.relu(z_5 + self.conv_block_6(z_5))\n",
    "        z_beat = self.flatten(z_6)\n",
    "        \n",
    "        if X_feat is not None:\n",
    "            z_feat = self.feature_head(X_feat)\n",
    "        else:\n",
    "            z_feat = X_rr\n",
    "        \n",
    "        z = torch.cat((z_beat, z_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class CNN1D(torch.nn.Module):\n",
    "\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "    \n",
    "    def __init__(self, n_features=0):\n",
    "        super(CNN1D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.n_features = n_features\n",
    "        self.beat_head = torch.nn.Sequential(\n",
    "            torch.nn.Conv1d(in_channels=1, out_channels=16, kernel_size=7),\n",
    "            torch.nn.BatchNorm1d(16),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool1d(5),\n",
    "            \n",
    "            torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3),\n",
    "            torch.nn.BatchNorm1d(32),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool1d(3),\n",
    "            \n",
    "            torch.nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3),\n",
    "            torch.nn.BatchNorm1d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.AdaptiveMaxPool1d(1),\n",
    "            \n",
    "            torch.nn.Flatten()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.feature_block = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 16),\n",
    "            torch.nn.ReLU()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.beat_head.apply(CNN1D.__init__weights)\n",
    "        self.linear_blocks.apply(CNN1D.__init__weights)\n",
    "        \n",
    "        \n",
    "    def forward(self, X, X_rr, X_feat=None):\n",
    "        X = torch.unsqueeze(X, 1)\n",
    "        \n",
    "        z_beat = self.beat_head(X)\n",
    "        \n",
    "        z = torch.cat((z_beat, X_rr), dim=1)\n",
    "        if X_feat is not None:\n",
    "            z = torch.cat((z, X_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class FocalLoss(torch.nn.Module):\n",
    "    def __init__(self, alpha=None, gamma=2., reduction='mean'):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.gamma = gamma\n",
    "        self.alpha = alpha\n",
    "        self.reduction = reduction\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "        # inputs => [batch_size, num_classes], targets => [batch_size]\n",
    "        \n",
    "        log_prob = F.log_softmax(inputs, dim=1)\n",
    "        prob = torch.exp(log_prob)\n",
    "\n",
    "        # Focal Loss formula\n",
    "        loss = -1 * (1 - prob) ** self.gamma * log_prob\n",
    "\n",
    "        # Apply class weights\n",
    "        if self.alpha is not None:\n",
    "            alpha = self.alpha[targets]\n",
    "            loss = loss * alpha.unsqueeze(1)\n",
    "\n",
    "        loss = torch.gather(loss, 1, targets.unsqueeze(1))\n",
    "\n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        elif self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "\n",
    "        return loss\n",
    "\n",
    "\n",
    "class MatrixWeightedCrossEntropy(torch.nn.Module):\n",
    "    def __init__(self, weight_matrix, reduction='mean'):\n",
    "        super(MatrixWeightedCrossEntropy, self).__init__()\n",
    "        self.weight_matrix = weight_matrix\n",
    "        self.reduction = reduction\n",
    "\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "        loss = F.cross_entropy(inputs, targets, reduction='none')\n",
    "        weights = self.weight_matrix[targets, torch.argmax(inputs, dim=1)]\n",
    "        loss = loss * weights\n",
    "    \n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        elif self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8fdf0e74-941b-477c-9c0e-860d48f5b405",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate_model(X_train, rr_train, y_train, X_test, rr_test, y_test,\n",
    "                             X_feat_train=None, X_feat_test=None, X_lt_feat_train=None, X_lt_feat_test=None,\n",
    "                             model_class=CNN2D, loss=torch.nn.CrossEntropyLoss,\n",
    "                             lr=1E-3, max_epochs=30, batch_size=1024,\n",
    "                             verbose=True, callbacks=None,\n",
    "                             class_weights=None, weight_decay=0., device=DEVICE, random_seed=0):\n",
    "    torch.manual_seed(random_seed)\n",
    "    \n",
    "    n_features = X_feat_train.shape[-1] if X_feat_train is not None else 0\n",
    "    n_lt_features = X_lt_feat_train.shape[-1] if X_lt_feat_train is not None else 0\n",
    "    \n",
    "    train_dict = {'X': X_train, 'X_rr': rr_train}\n",
    "    if X_feat_train is not None:\n",
    "        train_dict['X_feat'] = X_feat_train\n",
    "    if X_lt_feat_train is not None:\n",
    "        train_dict['X_lt_feat'] = X_lt_feat_train\n",
    "        \n",
    "    test_dict = {'X': X_test, 'X_rr': rr_test}\n",
    "    if X_feat_test is not None:\n",
    "        test_dict['X_feat'] = X_feat_test\n",
    "    if X_lt_feat_test is not None:\n",
    "        test_dict['X_lt_feat'] = X_lt_feat_test\n",
    "\n",
    "    kwargs = {}\n",
    "    if loss == FocalLoss:\n",
    "        kwargs[\"criterion__alpha\"] = class_weights\n",
    "    elif loss == MatrixWeightedCrossEntropy:\n",
    "        kwargs[\"criterion__weight_matrix\"] = class_weights\n",
    "    else:\n",
    "        kwargs[\"criterion__weight\"] = class_weights\n",
    "\n",
    "    model = skorch.NeuralNetClassifier(\n",
    "        model_class,\n",
    "        module__n_features=n_features,\n",
    "        module__n_lt_features=n_lt_features,\n",
    "        criterion=loss,\n",
    "        optimizer=torch.optim.Adam,\n",
    "        lr=lr,\n",
    "        max_epochs=max_epochs,\n",
    "        batch_size=batch_size,\n",
    "        train_split=predefined_split(Dataset(test_dict, y_test)),\n",
    "        verbose=1 if verbose else 0,\n",
    "        device=device,\n",
    "        callbacks=callbacks,\n",
    "        iterator_train__shuffle=True,\n",
    "        optimizer__weight_decay=weight_decay,\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    _ = model.fit(train_dict, y_train)\n",
    "    \n",
    "    y_true, y_pred = y_test, model.predict(test_dict)\n",
    "    bal_acc = balanced_accuracy_score(y_true, y_pred)\n",
    "\n",
    "    if verbose:\n",
    "        conf_mat = confusion_matrix(y_true, y_pred)\n",
    "        cm_train_plot = ConfusionMatrixDisplay(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1), display_labels=[num_to_label[i] for i in range(len(allowed_labels))])\n",
    "        cm_train_plot.plot(colorbar=False)\n",
    "        plt.show()\n",
    "    \n",
    "        print(classification_report(y_true, y_pred, digits=4))\n",
    "    return model, classification_report(y_true, y_pred, digits=4, output_dict=True), bal_acc, y_true, y_pred\n",
    "\n",
    "\n",
    "def balance_data(X, rr, X_feat, X_lt_feat, y, noise_std=None):\n",
    "    classes, counts = np.unique(y, return_counts=True)\n",
    "    max_count = np.max(counts)\n",
    "\n",
    "    X_oversampled = [X]\n",
    "    rr_oversampled = [rr]\n",
    "    X_feat_oversampled = [X_feat]\n",
    "    X_lt_feat_oversampled = [X_lt_feat]\n",
    "    y_oversampled = [y]\n",
    "\n",
    "    if noise_std is not None:\n",
    "        X_std = X.std() * noise_std\n",
    "        rr_std = rr.std() * noise_std\n",
    "        X_feat_std = X_feat.std() * noise_std\n",
    "        X_lt_feat_std = X_lt_feat.std() * noise_std\n",
    "\n",
    "    for class_id in classes:\n",
    "        class_indices = np.where(y == class_id)[0]\n",
    "        num_to_add = max_count - counts[class_id]\n",
    "        indices_to_add = np.random.choice(class_indices, num_to_add)\n",
    "            \n",
    "        X_selected = X[indices_to_add].copy()\n",
    "        rr_selected = rr[indices_to_add].copy()\n",
    "        X_feat_selected = X_feat[indices_to_add].copy()\n",
    "        X_lt_feat_selected = X_lt_feat[indices_to_add].copy()\n",
    "\n",
    "        if noise_std is not None:\n",
    "            X_selected += X_std * np.random.standard_normal(X_selected.shape)\n",
    "            rr_selected += rr_std * np.random.standard_normal(rr_selected.shape)\n",
    "            X_feat_selected += X_feat_std * np.random.standard_normal(X_feat_selected.shape)\n",
    "            X_lt_feat_selected += X_lt_feat_std * np.random.standard_normal(X_lt_feat_selected.shape)\n",
    "        \n",
    "        X_oversampled.append(X_selected)\n",
    "        rr_oversampled.append(rr_selected)\n",
    "        X_feat_oversampled.append(X_feat_selected)\n",
    "        X_lt_feat_oversampled.append(X_lt_feat_selected)\n",
    "        y_oversampled.append(np.ones(len(indices_to_add), dtype=np.int64) * class_id)\n",
    "\n",
    "    return (np.concatenate(X_oversampled),\n",
    "            np.concatenate(rr_oversampled),\n",
    "            np.concatenate(X_feat_oversampled),\n",
    "            np.concatenate(X_lt_feat_oversampled),\n",
    "            np.concatenate(y_oversampled))\n",
    "\n",
    "\n",
    "def print_results(reports, num_to_label=num_to_label, allowed_labels=allowed_labels):\n",
    "    precision_d = defaultdict(list)\n",
    "    recall_d = defaultdict(list)\n",
    "    f1_d = defaultdict(list)\n",
    "\n",
    "    for report in reports:\n",
    "        for i in allowed_labels:\n",
    "            precision_d[num_to_label[float(i)]].append(report[str(i)]['precision'])\n",
    "            recall_d[num_to_label[float(i)]].append(report[str(i)]['recall'])\n",
    "            f1_d[num_to_label[float(i)]].append(report[str(i)]['f1-score'])              \n",
    "    \n",
    "    for i in allowed_labels:\n",
    "        label = num_to_label[i]\n",
    "        print(f\"{label} -> Precision: {np.mean(precision_d[label]):.02%}±{100*np.std(precision_d[label]):.02f}; Recall: {np.mean(recall_d[label]):.02%}±{100*np.std(recall_d[label]):.02f}; F1-score: {np.mean(f1_d[label]):.02%}±{100*np.std(f1_d[label]):.02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ecb321cb-b1b4-4b84-b701-321d2eed153e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0\n",
      "  epoch    train_loss    valid_acc    valid_acc_bal    valid_loss    cp     dur\n",
      "-------  ------------  -----------  ---------------  ------------  ----  ------\n",
      "      1        \u001b[36m2.3009\u001b[0m       \u001b[32m0.4841\u001b[0m           \u001b[35m0.6514\u001b[0m        \u001b[31m0.8961\u001b[0m     +  2.9839\n",
      "      2        \u001b[36m0.7877\u001b[0m       \u001b[32m0.7243\u001b[0m           \u001b[35m0.7922\u001b[0m        \u001b[31m0.6396\u001b[0m     +  3.2938\n",
      "      3        \u001b[36m0.5129\u001b[0m       \u001b[32m0.8233\u001b[0m           \u001b[35m0.8268\u001b[0m        \u001b[31m0.5383\u001b[0m     +  3.3004\n",
      "      4        \u001b[36m0.4004\u001b[0m       \u001b[32m0.8594\u001b[0m           \u001b[35m0.8456\u001b[0m        \u001b[31m0.5095\u001b[0m     +  3.3001\n",
      "      5        \u001b[36m0.3276\u001b[0m       \u001b[32m0.8621\u001b[0m           \u001b[35m0.8581\u001b[0m        \u001b[31m0.4813\u001b[0m     +  2.9868\n",
      "      6        \u001b[36m0.2832\u001b[0m       \u001b[32m0.8679\u001b[0m           \u001b[35m0.8629\u001b[0m        \u001b[31m0.4591\u001b[0m     +  3.3068\n",
      "      7        \u001b[36m0.2538\u001b[0m       \u001b[32m0.8770\u001b[0m           \u001b[35m0.8674\u001b[0m        \u001b[31m0.4406\u001b[0m     +  3.3095\n",
      "      8        \u001b[36m0.2306\u001b[0m       \u001b[32m0.8834\u001b[0m           0.8664        \u001b[31m0.4278\u001b[0m        3.3049\n",
      "      9        \u001b[36m0.2122\u001b[0m       \u001b[32m0.8863\u001b[0m           \u001b[35m0.8718\u001b[0m        \u001b[31m0.4167\u001b[0m     +  2.9771\n",
      "     10        \u001b[36m0.1975\u001b[0m       \u001b[32m0.8902\u001b[0m           0.8707        \u001b[31m0.4122\u001b[0m        3.3015\n",
      "     11        \u001b[36m0.1845\u001b[0m       \u001b[32m0.8906\u001b[0m           \u001b[35m0.8745\u001b[0m        \u001b[31m0.4034\u001b[0m     +  3.2848\n",
      "     12        \u001b[36m0.1736\u001b[0m       \u001b[32m0.8923\u001b[0m           \u001b[35m0.8797\u001b[0m        \u001b[31m0.3961\u001b[0m     +  3.3037\n",
      "     13        \u001b[36m0.1630\u001b[0m       \u001b[32m0.8954\u001b[0m           0.8780        \u001b[31m0.3939\u001b[0m        2.9843\n",
      "     14        \u001b[36m0.1541\u001b[0m       \u001b[32m0.8959\u001b[0m           \u001b[35m0.8821\u001b[0m        \u001b[31m0.3877\u001b[0m     +  3.2914\n",
      "     15        \u001b[36m0.1467\u001b[0m       0.8957           \u001b[35m0.8852\u001b[0m        \u001b[31m0.3845\u001b[0m     +  3.3035\n",
      "     16        \u001b[36m0.1390\u001b[0m       \u001b[32m0.8989\u001b[0m           \u001b[35m0.8894\u001b[0m        0.3858     +  3.3040\n",
      "     17        \u001b[36m0.1326\u001b[0m       \u001b[32m0.9008\u001b[0m           \u001b[35m0.8909\u001b[0m        \u001b[31m0.3828\u001b[0m     +  3.2934\n",
      "     18        \u001b[36m0.1270\u001b[0m       \u001b[32m0.9029\u001b[0m           \u001b[35m0.8969\u001b[0m        \u001b[31m0.3806\u001b[0m     +  2.9856\n",
      "     19        \u001b[36m0.1214\u001b[0m       \u001b[32m0.9041\u001b[0m           \u001b[35m0.9001\u001b[0m        \u001b[31m0.3765\u001b[0m     +  3.3086\n",
      "     20        \u001b[36m0.1157\u001b[0m       0.9040           \u001b[35m0.9035\u001b[0m        0.3770     +  3.3024\n",
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      "     22        \u001b[36m0.1069\u001b[0m       \u001b[32m0.9052\u001b[0m           \u001b[35m0.9089\u001b[0m        \u001b[31m0.3689\u001b[0m     +  2.9876\n",
      "     23        \u001b[36m0.1029\u001b[0m       0.9044           \u001b[35m0.9103\u001b[0m        0.3703     +  3.3057\n",
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      "     26        \u001b[36m0.0917\u001b[0m       \u001b[32m0.9091\u001b[0m           \u001b[35m0.9150\u001b[0m        0.3695     +  2.9868\n",
      "     27        \u001b[36m0.0882\u001b[0m       \u001b[32m0.9105\u001b[0m           \u001b[35m0.9162\u001b[0m        \u001b[31m0.3678\u001b[0m     +  3.3070\n",
      "     28        \u001b[36m0.0849\u001b[0m       \u001b[32m0.9107\u001b[0m           \u001b[35m0.9174\u001b[0m        0.3687     +  3.3096\n",
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      "     31        \u001b[36m0.0766\u001b[0m       \u001b[32m0.9146\u001b[0m           \u001b[35m0.9214\u001b[0m        \u001b[31m0.3652\u001b[0m     +  2.9893\n",
      "     32        \u001b[36m0.0741\u001b[0m       0.9145           \u001b[35m0.9228\u001b[0m        0.3671     +  3.3151\n",
      "     33        \u001b[36m0.0714\u001b[0m       \u001b[32m0.9153\u001b[0m           \u001b[35m0.9235\u001b[0m        0.3728     +  3.3033\n",
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      "     36        \u001b[36m0.0646\u001b[0m       \u001b[32m0.9163\u001b[0m           \u001b[35m0.9252\u001b[0m        0.3747     +  3.3190\n",
      "     37        \u001b[36m0.0623\u001b[0m       \u001b[32m0.9167\u001b[0m           0.9248        0.3748        3.3111\n",
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      "     49        \u001b[36m0.0444\u001b[0m       0.9205           \u001b[35m0.9281\u001b[0m        0.3892     +  3.3082\n",
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      "     51        \u001b[36m0.0419\u001b[0m       \u001b[32m0.9244\u001b[0m           \u001b[35m0.9282\u001b[0m        0.3947     +  3.2993\n",
      "     52        \u001b[36m0.0412\u001b[0m       0.9208           0.9275        0.3977        2.9930\n",
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9948    0.9239    0.9580     44218\n",
      "           1     0.3514    0.9205    0.5087      1836\n",
      "           2     0.8944    0.9444    0.9187      3219\n",
      "\n",
      "    accuracy                         0.9251     49273\n",
      "   macro avg     0.7469    0.9296    0.7951     49273\n",
      "weighted avg     0.9643    0.9251    0.9387     49273\n",
      "\n",
      "Time: 441.03s; Balanced accuracy: 92.96%\n",
      "N -> Precision: 99.48%±0.00; Recall: 92.39%±0.00; F1-score: 95.80%±0.00\n",
      "V -> Precision: 89.44%±0.00; Recall: 94.44%±0.00; F1-score: 91.87%±0.00\n",
      "S -> Precision: 35.14%±0.00; Recall: 92.05%±0.00; F1-score: 50.87%±0.00\n",
      "Balanced accuracy: 92.96% ± 0.000000\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    #LRScheduler(policy=StepLR, step_size=20, gamma=.5),\n",
    "    #EpochScoring(scoring=make_scorer(f1_score, average=\"macro\"), lower_is_better=False, name=\"valid_f1\"),\n",
    "    EpochScoring(scoring=make_scorer(balanced_accuracy_score), lower_is_better=False, name=\"valid_acc_bal\"),\n",
    "    Checkpoint(f_pickle='models/checkpoint.pkl', load_best=True, monitor='valid_acc_bal_best')\n",
    "]\n",
    "\n",
    "n_iterations = 1\n",
    "\n",
    "n_features = 5\n",
    "\n",
    "class_weights = torch.from_numpy(compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)).to(torch.float32).to(DEVICE)\n",
    "\n",
    "reports = []\n",
    "balanced_accuracies = []\n",
    "predictions = []\n",
    "for i in range(n_iterations):\n",
    "    print(f\"Iteration {i}\")\n",
    "    ts = time()\n",
    "    model, report, bal_acc, y_true, y_pred = train_and_evaluate_model(\n",
    "        X_cwt_train, rr_train, y_train, X_cwt_test, rr_test, y_test,\n",
    "        X_feat_train=X_feat_train[:, :n_features], X_feat_test=X_feat_test[:, :n_features],\n",
    "        max_epochs=200, class_weights=class_weights, loss=torch.nn.CrossEntropyLoss, weight_decay=0., lr=1E-4, batch_size=4096,\n",
    "        model_class=CNN2D, callbacks=callbacks, verbose=True, random_seed=i\n",
    "    )\n",
    "    balanced_accuracies.append(bal_acc)\n",
    "    reports.append(report)\n",
    "    predictions.append((y_true, y_pred))\n",
    "    print(f\"Time: {time() - ts:.02f}s; Balanced accuracy: {bal_acc * 100:.02f}%\")\n",
    "pkl.dump(predictions, open('results/experiment_3.pkl', 'wb'))\n",
    "print_results(reports)\n",
    "print(f\"Balanced accuracy: {np.mean(balanced_accuracies) * 100:.02f}% ± {np.std(balanced_accuracies) * 100:02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d146a1f8-d629-4938-925b-55cabf554166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9732    0.9815    0.9773    153542\n",
      "           1     0.1107    0.6975    0.1911      1990\n",
      "           2     0.8235    0.3356    0.4768     20000\n",
      "\n",
      "    accuracy                         0.9047    175532\n",
      "   macro avg     0.6358    0.6715    0.5484    175532\n",
      "weighted avg     0.9464    0.9047    0.9114    175532\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_dict = {'X': X_cwt_incart, 'X_rr': rr_incart, 'X_feat': X_feat_incart[:, :5]}\n",
    "\n",
    "y_true, y_pred = y_incart, model.predict(test_dict)\n",
    "bal_acc = balanced_accuracy_score(y_true, y_pred)\n",
    "\n",
    "conf_mat = confusion_matrix(y_true, y_pred)\n",
    "cm_train_plot = ConfusionMatrixDisplay(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1), display_labels=[num_to_label[i] for i in range(len(allowed_labels))])\n",
    "cm_train_plot.plot(colorbar=False)\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_true, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2f257371-ed3a-4874-907b-1723337e18e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_incart = z_train[-y_incart.shape[0]:]\n",
    "z_mit = z_train[:-y_incart.shape[0]]\n",
    "y_mit = y_train[:-y_incart.shape[0]]\n",
    "z = train_test_split(z_incart, y_incart, stratify=y_incart, train_size=.05)[0]\n",
    "z_incart_size = z.shape[0]\n",
    "z = np.concatenate([z, train_test_split(z_mit, y_mit, stratify=y_mit, train_size=.05)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "11373a5f-01c6-4e0a-b4bc-f65ce902ef4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_jobs=-1)\n",
    "#z, _, y, _ = train_test_split(z_train, y_train, stratify=y_train, train_size=.1)\n",
    "z_transform = tsne.fit_transform(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9021b953-230c-4faf-988f-ecd5e94c38c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C9jrkLt3dHZG7ULil8GngTaV1QeIP5DPqorH+eqnyuhY1irejopHSmmZyk2MpdjjZUl7P9kMNAGw/1EBhcQ2Tc5M4UC/7Z2YkRDMiJZbi6mZ1f2V1Lcx5ah3jsuy8detZVDS4RH3giUJnN619zM9+9jMWLVoEwJIlS3q8n8zMTLZu3ao+TkpKUv+fnp7OiBHyd3B+fj5NTU1ce+21/P73v1eXB2I0GsM+dywQAlAgCEekQq2rdcLtOxLBFo6eCE/ldX/+idyU8tYNem/B+etFx3B/p74MqjW/s5RRoX/3bU7YuRIK5kG0P8VUWtPM3ipn8Poh+NGyDWx9cE5I/77JuUnYLCZa2z3YLCZev2kyuw83csm4DOJsUWpqNyYKUmItVDfLljV3v7kVn9fLrsP6YzAAP33la3WfADaLkdZ2L3mpcbS5Jco1zSXbDzVw6eIv2Otojih6KOgH9PSmtRe44IILcLvdGAwGfvCDH/R4P2azOWLBZjKZAGhtbe3x6/U14tteIOiMSITa0Yq5ntBT4alsazQHG0sL38D+j32ILPqqO+oATdHB67Q54dEhgA8+vB3uP6gTgeEwG0Eb9HO1e9lYUsu5o9KC1rVGmdny4Gw2ldYxOTcJa5SZiacMAvSp3UB2dkQDA1Hi0NqoYmu7fDBFDicjU2ODttnrkCOD2w818NkeB0aDgTNzBuFwuv1RweOQchSEoac3rb2AyWTi+++/V//fFxw5coTDhw/j9XopKirid7/7HSNHjmT06NFdb3ycEH8RAsGJytEIz0E5ctNKZccdecaEY/qFLOgBHgleucAv/kCuBQwcAbdzJX5J5ZMfT/oJII9lG5uZwI6KRqxmIy7JyyCbid9cMoZZo9OYt7SQomp/WjjDbkOLtmnDGmVmZn5q0GGW17WEFH8gp3WrGtsITC5bjNDuBavFiKtD+EWbjbRJXkamxXUatbQY4aa/b9YtS42PZuk14yj4+Aqs1duEJ2Z/4GhuWnuBhISEPt2/0lms2MbMmDGDP/zhD5jN/fecM/h8vi6NzhobG7Hb7TQ0NPT5hygQCI4RgSPuxMWxf1NTLE+gCWTBJr0A1EYAMQRFABURlxoXxebyI2oED+TmjkueWU9RTQsFmfG8t3CamlqNtGmjswhgV1hMMDTRRkmtnDYbkRIL+CiuDl+rGI5cQyWfRt+rPm658i1iRs0S53k3cLlclJaWkpubi9UqPEL7C539Xrqj18RfgkAwUAnsLhb0b7QpNItNni6TMSF47nN0nCz6QtQAAphNRrKTYlSRlp8ax/uL5Fm91igzH909M6Q1izayt/1QA6U1zZiMBnU9bXRwxYIplNe1kGA18vt/7ea9bZVEQrsHVfwBugaQ7lLuS2WbdxjjjSW0+KKIefsqpPQJmG9eI0SgQIAQgAJB/0bUMAkUtCk0+xC/H2So8yI6Tk37KkgeL6U1fkGliLk9DidzlxSy6g7Z989sMjIsJbhmMDsphnFZdrYfamBESgw/e2UTB460kZccw8qFU/nxi1/pooPDUuJwuSUuGpfJv3dU4g7I+0abDbRJPZy0EwEeTFzu/i1TjLv4R9QfATAf3kpb1R6iM8f02esKBCcK4ooiEPQHQgm9dhe8cI7sMyhqmASgr/vsRv2nMlVDsWQpyExgZGqs2kixp0r2/Wtrl9hS3sDEbDujMuxBKd7HrxjHwte/1UXmimpauOiZ9eyvl7t0tx9qoNjh5EBdC7cv34Kr3Uu02cizV43DbDLS1u6htLaZn5w1lMMNbr4uq+PFdfs41Cjb22iFYZTJoJpTB30UwP9dkM8jH/vntP6/H47i1Y1lHKh3MWywjQN1rWzwjmG7N5dxxlIAyl74CcMf+BKzRRhKCwY24moiEBxvtP5YqafCzZ/KXbrPn+O3/KjYElzsLxBEiHaqBsjduB/fOZ07lm9hb5WTgswE7lq+hV2VTeo6YzITeH/hVDW9qxWQgeyvd6lNGzaLkcuWFOLStBS3SV7ueXsrWuvAp9cUMyIljuJqucEjIyEas8HHgQa/z+Gz107kif/uYa+jmbFZCVw1aSgPfrALAA8EWdTMzE/lhqm5aipa8nhZu9vB79+/k7c8dwGQ79vHgbLvGZo3vkefpUBwsiCMkwSC443WH8vxHTw7Cb5cpvd7A3jnJmHVIug2ksfL3W9u1S3LGRxDekIUUR0RvjbJoxN/ALsqGimvk5svAgVkKNo6BF9ru1cn/hRC+Ear4g+gsrFNJ/6izUZu/ee3RFvMrL57BisXTGXexEzd9su/LqcgQ55WMjYrQZ0QMiwlDrPJiDXKzA/HZfL3X17LHqMcMd1ryiMjp/9acwgExwoRARQIjjeDcuTIn+M7+XHjQVj9q+D1HDtO7ihgu0s/Zk/QczQlBeV1riD/vbLaFs54ZA0djisUOZoZnhLLPk1qd0xmApl2KyXVTtraI7/xsFmMeL0+2gJSt4qxcyTkDo6htFYWnzsONWAyGjCbjDicbt16uyqbWH33DF0zSiisVivD79/IgbLvGZYzWqR/u0kEZiGCY0hv/T6EABQIjjcms5z2fXaSLP4GGh5J9rZ7abbc2Wqxwf+WCRHYUwJGbmXf+F/V+09LoBYzdHgHZsRHsewnkzhlcAwXPr2e0tqWkEbM4Wht9/LRHdNwNLUxKTuRysY2AIYOsrGvupkFr21mf12r6kM4IiWG8rpW3B4fNouJlQumkDM4hque+1JuKhliJzspBpAbUbTvZWyWPeK5wGZLlEj7dhOLxQJAS0sLNputi7UFx4qWFvnmSPn99BQhAAWC/oDFCte8Ds/PCL9Oyuhgy4/e4nh1G3skeHEWVG71L2tvlSOBEc63FQQQMHLL3FDO2/PP5gdPfa42agSSmRCleu1VNrkxGY1MfvRTNa2719HMKYOsIbe3WYy8M38K9727nZ0VjeQkxTB0kI28tHjK61pUgeZyS1yxbCOt7R4sJgNf3DeTRpdXrdXTThUBVCsZbWTPbDKycuFUtZs5UvEn6Bkmk4nExEQcDgcAMTExGAyG43xUAxefz0dLSwsOh4PExMSjnmoiBKBA0F9IGyP7ulVuhUG5YIyC2o4Ox5TRcMtnvSvOFCNoqQ1W3irXHKaOhps/O3bRt+rdevEHcgQwZ+qxef2TkRAjtw7UtAaJNwOyVXS02chfr5/E3KUb1efe/LpcFX8KcVYLeakmijo6h0ekxHDfBaOZnpeM2WTE55XXL6tr4Yw/rGF4ciw7K5tUW5hNpXXqqLd2j48rl21i9T0zVeuZwKki4exozCYjeWnxR/khCSIlPT0dQBWBguNPYmKi+ns5GoQAFAj6CyYz3LTGH4mDvpvUESryBrLlzF+nwG0b+l4EeiR49yb9soufhvHXDLj0r9aj76ijWh1+gVJtCaWeFKhppS2gA+O56yYydUSyOgkE5Kkbir3LPzYdCNqtUm+noD3Okmonuw77Gzpa273s7Ggq2X6ogfK6FiZlJ+r2V1rbQnldS0iRJ+g/GAwGMjIySE1Npb29/XgfzoDHYrH02jxjIQAFgv5E4Hzfvmj48EhQtDpY/CnU7ZMtaG75TBaj0LsC1CNB9R4oLdR3OputMPaKzg2OT0Ikj5d5Szewo8OY+dS0WH40aSj1TjdzCtIYk5XYbUEoYWTe8ip2VBQBkJeqF1nDUuOxRpnJTLRR7HBy1/ItFNcEj1u7Z3Ye//2uip0VjYwbEr7eLjsphoKMeFX02SxGfwSwo4ZP6ShWyE+LV2v7BP0fk8nUa8JD0D8Qs4AFgoGER4IXzoPD24KfM1rAq7nDT86Hmo4UdMZEuKkXjKg7e33wd0MPIOPrkmon5z3xedjnT82I5+lrJoYUX9rxa9qZvYXFNfz0la916+alxFJULfvpvXHTZC7/6wbVCDoU0SYD2x46H7PJGHI0XCCSx0uxw0llQytnDxsctJ12RnB+WhzvL5wa5OMnEAiOju7oNSEABYKBhGM3LJ0c+rm4THBWhN920eZuTZ8IyeGdsCxMfV/ScDn62JuvdwIQGAEMx9gsO09eNZ6hg2wUO5rYeqCBNzbtZ9dhpzrP12wyqiLLajbg0oxaG5MRz1+umcjQQTbmLi5kj8MZ8nUy7dE8dMkYZo5M6XWBFkqwCgSC3qM7ek3cfgkEAwHFYy82Ofw6zgrU1oD0CfJPJVKXMdFfl9gdtN3FAO/+PPR6yflw81p49WJd88JAwGwysnLBFEprmml1e5i7pJBQd+U7DjUw56l1qn2Klj0OJ5cuKWTJj09TZ/y6Aubs7qpswmQ0UNHgCiv+8lJi+fD2aX0WmQvX2CEQCI49QgAKBCc7bU54PBc8bjBZIX28LOwGDYOoOKjarlnZBxc9DROukYWbV5LH0kVaA6gIPvsQqN0H79wop5EzJsDcJXLXr0LCEDj/D5A8HFJGqc0Lx8WO5jij7Wzd8dAc3t58gNpGN+eemsJvPvhe5+EXasoGwN4qJy1tkmq4bLMYGZYcq074GJvl99PLT43TicAXfjKJnORYYasiEAwgRApYIDiZ8Uiw+AyoL/Evu+JViI73W61s/jt8/Ev/87esg3/d6Y/EdVWLp9jJeD3w/gK5ucRsBSnAM25wvt/WxhwNvyyB6P4ZDVK6ctvaPRxudJGVKPvaHQ9xpBzLPW9uZUdFI1aLEVeYiRqDbGbqW/1TOz66Y5p6zFpx53JLzF1SyJ4qJ+OG2Flx2xQh/ASCkwCRAhYIBDK1xXrxB7DmN1BfKqd5PS45KmeMBm+bHB00R+uMhCn5HIbN1ItA1UPQBStuhZrd+tcIFH/gF38gew/Wl0F6QS+8yaNHEVker4+WtnYWvf4tFY36sWMFmQm8t3DqMRdKSnRw5cKplNe1kGm3UuxoYvvBRsZkxuv8+7TiD8DrQ+eZ52x1s2rHYS4am86qO6aLejyBYAAjIoACwclKuwuen6lPu3ZF8ii4eY2/Fs9ikydzZE6EGz/2p4XfWxC+kzcUxihIyvV3FYNsbj3/i+Oa6lWE393Lt6gWJp3x8Z3TGZXRf74DXW6J8b/7L21S6K9xm8XI1w/MwuF0kxRjZuLDa/AhV3rueGgOcTYxE1cgOJkQEUCBYKDjkeCFc4PFX9p4MBjg8NbQ29V0zOS9+TO5aeS1y+XlFVtgyVlwpLTz100fB5522d8vZRTMXQrNtTCsw0B46dn+iGT197KgPE6dvlpbkkiZt7SQLQ/O6Tf2JQfqW8OKP5ANmS9ZXEhpbQtZiVa1ucQHrNpxmKvPzD4mxykQCPof/eNbTCAQ9Jx2F5Sug4RMuVmjvgyOlMt+egopo+HKv8nP1xbrrWAC/f8c38tmzMNm+keKmaK7Fn8Al7/gP4ZQjRwLNsrCVPH6O46dvuV1Ld0SfyALqk2ldUFjy/oTUSYDRoPcBRxtNlJaKxswHzriT8sbgIvGHv0oKYFAcOIiBKBAcCLT7oLHc+Q0LchCzdMm/19J36aeCjd/6h+vNniEX9iljIZ5y+TlK+fLUTlFmClduSWf+yOBCsmj4PLn4N1b/LV9GRP83cLhonoWK9y6vl90+mYnxTAuy872Qw2MyYinqKoJd+jeChWbxaSOTusP5CbH6iZwALg9Pj6+czpVjS4mZSfy4xe/ChK6v7nkVFH3JxAMcEQNoEBwIlO0JlicabluRXADB/ibOFbeKnftpp4KP18NTYeDhVm7Cx47xd/YkZIvdwpbrP79QO/PKz4GKMbEHq+POU+t63TdR+cVMDF7ECNS4/qVeFImcNyxfAt7Q3T1OlvdfLi9kn9uLNPN67VZjP0qnS0QCI4eUQMoEJzIaM2TuxJUOVP1livaCGDGxNDiT6HhkH8esOM7ufbv1hBNGQ0H9V29V/7dH000mftmXvExQjEmljxeCjIT2Nnht2c2gLa0Li8lltc3lXP/yp2My7KzYsFxtk3RnCMut5dtB47w1i2TqWuRdF29LrfEGX9YS2u7B5vFSKY9mooG+fxobfeysaSWc0elHb/3IRAIjhtCAAoE/QmPJAuxii3BqdtQGM0weKRs5jx4JNzyqSzYIHxETvsaWvHoCNOUEZ+OOiEEg2zyfJJhNhl5b+FUiqqaqGxwcWbOIK59YRM7KhoZmRrHM9dO5IKn1wOw/VAD5XUtx2+iheb350mfwISye5Ew8X8rYMuDs1SrmIoGF/trm2lt9wCy4GvtEH8KGXbb8XgHAoGgHyAEoEDQX/BIcr2d4sHn+A5eOCd0VE6herd/kkft3si89erL/K8huSBpGNSVhG/KKN8E2v7R8k2QN6s77+yEwGwyMjrTzuhMO4Dqu5caF8XX++vVCOG4If6JGscFze/PdHgrQw0OSn0Z+IDznlxHXXM7JgN4fDAmPQ6bxaRGALWTQcZkxDMitX8acQsEgr5HCECBoD/gkeDF2VC5BX+0jfBROYXGiuDHXQnAQTn+JhDF36/hYHDKWUkzZk/2N5RYbP4JIic5ZpORTLuViQ9/ogqoj++c3mc1gEo9YpfGzJrfnyd9AgfK/B3Jdc1yN7en4/TZddjJqtunUeNsY3JuEmaTkdKaZgAx9k0gGOAIASgQ9AdqizvEH/ijbUDq6M6tUnJn6MVZ7oyuXyvUzN1Agdl6BJZNh4ZyWWz8oliO/OVM7TwlfQKhnYoRzhB5U2mdLoVa1ejqEyPoI80uLn62kINHXF3XGGp+f6ZBOayocHLpksKw+44yG3W2NdrJIAKBYOAiBKBA0F9RagA7awSxWOF/y2TT5u6Is86sWtqcctevQsUWuTv4JEr7Hml2qVMx/m/FjrBTMSbnJmlSqH1jAXOk2cWEh9eojyOqMez4/UkeL/ev2B52tVPT48hNju3NwxUIBCcJIv4vEPQHBo+AlIDU7bRfRratxSqLM4tVtmwpWiP/7AkeCb5cpl9mzz6uhs2d4XJLrNh8gJ+/8hUH67se5QZyqvXiZwuDpmKEwhplZsuDs3n1xjPY8uDsXrdMkTxeLnp2g27ZkERrxDWGxQ6nWtOnxdLxzW4yiq94gUAQGhEBFAiOJx4JqnZCxVbImwPVO/3PrbhRtnX50cuQlCOPVussGtjmhD8Nlxs7LDY5TdjVNlraXXLTieN7/fL56/ulv1/gHNw1j1Xzyb3TMGIKskLZVFrHpOxEKhvbOFjfwsFuTMWwRpn7bPJHaU0zh4606pb96/apEdXmSR4vdyzfErQ8J8lGWZ28zx0Vjce3Y1kgEPRb+t+3ukAwUGhzwpKzobG8Y4EheB1PG7x1nfz/9PFw89rwps5vXOu3dGlvhWVT5fq9n3/StYBTZgdrxd+5D8JZ8yG6f4qHTaV1QXNw5zzxBT5Q6+gkj5cJv1uNS9KP+FDSukMSbfzr9ilhawCPNe8vmEJibGRp/NKaZvZWOYOWl9W1kp8axx6Hk/y0eDLtJ0fNpkAg6F1EfkAgOB60OeGxXI34A/BBfFb4bQ5vkxs3tCiecEsnQ31J8DYVW4K3CUV9mX52cOpomHZXvxV/INfnRZv1olmRg0od3YZ9tUHiD6C13cMrN5zOZ788J2LB1du43BIH6loYkyE3ZYzNSmBMlj2ibSWPl3ve3Ko+1n4K0SYDb946WRaBVU1c8NQ6tpXXUVTVhOTpYtadQCAYMIgIoEBwrGl3yYLN6w5+zhwL8wvlmruS9fDujSB1pAjTxwfX4mk9/UIxaFhk9Xtaa5hImk/6AdYoM9t+fT7/2lbBbz7chdPtVSN7ildfW0cHbyie/O9epuelHMMj9nOk2cXpv1+D5IMoE3x0xzTy0uI7Tf1qbWLK61rY0TG1BHR947R5fGw72MgehxwdLKtvZe7SjQCMzbKz8nhPMREIBP2C/v0NLxCcbCipVmVaRyD1e2HFzXDrehh9IdxXBqXrICEzdD2ffYjfBkbrH6jwoxciE3KhrGFOAKxRZq44I5vLThuim4Ch1ADmpcWrBs6Bn87xqo9ztrpV8Qfg9sCew02qAXUoJI+Xy5duYPuhBnIHx7BywdmMy7Kz/VADYzLi+K7Sqb63MRlxTM5NIjcphtK6Ft1+dhzvKSYCgaDfIG4DBYJjSWCq1WgJXsfxnT9ta7HCyPNlc+dQoqy+rEP8Afjgmjcgdaz/+VW/kEVnJCjWMP1U/EkeLyXVzpBpTGWmrzXKzLAUv1GzMuLtscvHBkrj4zLRQ+5A/oKA0kV+8fY2XG5Jt15RVZOati2va2H7oQYASmtbOOORNbx8w2msvXcmf7nmNN17e+xH49lUWsfbt00myqRPkY/NOs5TTAQCQb+hf37TCwQnK4Gp1p+vlqOB7mb42w/9HbyRztuVAuxeEjLhihflFDPI5tK1xZA6yr9Ou6v7voHHCUX8pMZF8aO/bmSPw0leSgx3z84nymJk2ojkLq1ZzCYjcydk8psPv1NNnfNSYnjrlrOOeSq02OFUO3S1SD65qWVmfiqSx8u8pRvY0SH4CjIT+POV48kZHENZrRzRa/fC2X/8jB0PnQ/4m1qsFiNXLNuAS/Jhs5j46oHz+La8gdT4aKItpoimf5TVNvKrd3fxyI/GkDO4902vBQJB/0AIQIGgN+lKXIVKtaaOgppifQdvw8HwRs1aGiv1j3evgpE/0KSFgbd+Crd+7vcJfDzHPznkf8v6rQjUpj2jzUbaOpo5iqpbWPCGXPdosxjZ8uCcLkWgNcrMygVTuODp9eo+KhpcxzQVKnm83P6Gvl7TCHhBZzJdXteiij+AnRWNXPD0erITo7EYZfEH0O7xsam0jsxEmypsXe3+6Ghru4dtBxs5b3RaxMdYVtvIOX+SP6Nz/rSez345XYhAgeAkRaSABYKjxSPJAq65Bv6YDa9d3iGywpgxh0q1KpFBkH9G0rjhkeCzR/XL1j0OL87SpIWBmt2yv59HgpJ1/ufaW2Wx2k/Rpj3bQnTygjyebVNpXUT7yxkcg81iAmTBdaztUUprmily+G1b8lJj2f7QnCCT6eykGMaG6AYuP9KGzweWjrRutNlIclw0Tpc+xR+lPm8gOS66W52/v3p3V6ePBQLByYMQgALB0eCR4MXZsHiSbMLsaZOXt7fCtuXdq7/7+SewaHPXvn2K4Kwtlq1hIsHxPVTvhs8e8S8zW+VIZT8lKabrBIXZSMTj2SoaXJq5vh4qGno4LaWHuANE7F+unog1ysxQjWk1yCnrlQumsPruGXx853Rykmzqc5IPHrhwFKck2WiTvFz07BfM+6t/ksip6XF89cB5nJIUQ5vkk59fUhixCHzkR2M6fSwQCE4ehAAUCI6G6t1ynV0o/nWnHI3rzSYMxfdv8SRYOR8G50d+rG9cA5Vb/Y9vWtuv07/zOqxLOuOL+2ZGPJ4tOymGcR2RtWPdACJ5vNz9pv48MRkNXL50A+c98TmXL92gE2lKB/OojATeWzhF5/P32399z/4QdYQAPz7rFA7Wt7Jf0/2rdDtHQs7gBD775XSmDksS6V+B4CRH1AAK+gxtAf/m8iNMzk3q9VmqxxWPBO/+rPN1KrfK9X6R1PNFgtb3r3ILDMqNfNuGA/7/Z0wEo0l+D/2w67e8roXS2vCiJTXWwr/vmkZyfOQizmwysmLBFNVLrzcaQLTefJ3tr7yuhb2OZvXxyLQ4TEaDmuLe3ok9S12LFNTBHI6JQwfxzX59SjwjIapbYjdncAKv3XJ2xOsLBIITk/73zS84odDOWXU43eqFUFvAr/iv2SxGVi6YyojUuJPDiLZ6j/yvM1JGR1bPFynaLuKU0VD9fZebBBGfBT6f3Ckc6ai4Y0x2Uoyu6zUQR3M7jS4vyfHd269iF9MbaM9xZfRcuPNaiT5uP9RAfloc7y+U5/0qyzqLSGYnxTAyLS7k2DctFiOckmTjq1K9XDQbT4K/NYFA0OsYfD5flzeXjY2N2O12GhoaSEgQKQGBjMstMfHhT2ht96gib2xmAo9eXsD2g4088N7OkNvlDLLxrzum9Zv5qz3CI8GXL8Dq/wu/TsIQuH1z76dZldm/K26Fw1u7WNkEhJ+GAch1h70VoexFdh1q4KJnv9Aty7JHc6ihjXFD7Ky47fhOtCipdnLeE5+rj1/+6enMGJkS9phCRQsjjSC63BKXLi5kr6NzETgyNVYXaVRYe+9MYf4sEAwAuqPX+tdtv+CEYmNJrVpUr9xF7Kho5OLFG8JvhDyaauLDq9nxmx+cmCnhNif8dSocKQu/jikKFm7qmxo7JVrXpfiDsOJPiR5G2nF8HMhPj2fYYCsltf5mjaXXTSLeZum1FO7RkJ0Uw9jMBHUk289e/YaxmQms7IjuBRIq+hhpRNIaZeatWycz58nPqW4OXVN6SpItpPgT5s8CgSAUIjcg6BGSxxvU1dgd2r3w+d7qXjyiY0S7S+727Uz8JQyB/y2F6D6KuHgkWHlrz7cfPBJu+SyyjuPjiNlk5N93zmREiixexmTEMybLrpv0cTwxm4w8efUE3bIdFY0UFtd0y3qlKySPl92VjUx+9NOw4g/g9nPzGJsZfMf/5FXj+8XnJRAI+hf985tf0G+RPF5Ka5q5+82t7NQMo1fQGtV2RbWzrZeP7hhQVhg8fUNLymhZXPVld219mb6btzOMFvC265fN+a18fP0w7RuINcrMx3fN7NXGjd4kNzlWnTUMYLUY+ekrX3dZExgpzlY3Fz9bSFmILt6kWAt1zf7f7cGGFlYunEqxw8ntr39LUXUzY7MSyE2OPapjEAgEJydCAAoiRlv0Ho5IxZ8BuGx8Zu8c2LEkZ6p/yoYpGs57COxZkJQj++oN7qVZuh5JPy1Ei7YRxBTt9x4MxU1r5G7fF2eD1DH9Y/h5R398x5DebNzoC7yaMmplEkdnXb2R4mx1c9rvP8HtCS7THpESyz9vOp2zHvXXIN5w9imYTUZGpMZpSisMQdsKBAIBCAEo6AalNc2dir/uMCw55sSs/7NY5fFpfTlLV/H6q9gSukvXZIYbP4alZ0F9qX7bxFw40rEsfTykjZHXv6+Pj/lkI8J5yaU1zXxX2RS0/Gjr7iSPl0ueLQwSf7+5+FTOGj5Y7aTf+uAs/vnlAa4/ayiJsfJxakfJ7egFISoQCE5O+lc+RdBvkTxe7nzjW/Wx1WLkwR+O6nK7oYNsIZfvq2mJ2Jy232GxQt6svhNSWq+/ii3y41DrBIo/i00WfymjYX4h3LzWLxz7+phPJpR5ya9dDo8OkUf8hUDyeNlfG9x0AXDnrBG43BKf73HgckdoBK6hvK6F0hB/H6ckxzAqI0FNLSfGWlk0K08Vf6A3vC7ITKCsprlHxyAQCE5uTsAQjOB4UOxw8t1hvwWFq91LSW3nlhQAB+pDTywoyEwQnYnh0KZ4M8ZD6RcQn955U4l9CDQclP9f/b2cju6nzR39nrJC/7xkb7vc9HP/Id3nL3m8XLb4C3aGiP4B3PT3zRr/S5Nu1q/W+gUIWd+o9Q1UsFlMnD1scJfWMYrhdVFVE/OWbuBnr34TdAwCgUAgvg0EEXGwPjga8dqmgxFvPzItjqeumkBlQytZiTby0uL7XUF/v0GZC1y1C56fAavuhFV3wf0H/SJk8AhIGSWPogNZ/J0A1i4nBDlTg5tndq6EST9RH5bWNAeJv9/PHcP/e3+X+lhJ3ra2e9hYUsu5o9J0dbRjMxPwATsrGnX2MYrAe+vWszhQ30pbu4dqZxtnDxsMwEXPrGdPlZO8lBj+94LRTM9LDhJ2ZpMRR1Mbro5O/dZ2D5tK65iZn9p7n5NAIDihEQJQEBGp8ZGnDgsy4/nTFeP533e2s6OiUZ18YI0yM6YjNSXoApMZKrdrFvj0IsRkhnnPwfMz/avMWwZRcaEbRwSRY7HCvbvlyB8ABiiY1+km0WYjl03I5M2vD6i+gFoy7HIpRLHDqUb1tOvtqGiktKaZ3ORYnUDEYGBHx6SRs28dzNzFhezpMIMuqm7h5n9sxmYxsuXBOUEicFJ2ohqFNHQ8DkSZ5HPSjWkUCARdIv7iBV0iebzc89aWrlcERiTbeGf+FKxRZlYunNpv7Tv6nM66eCOlYB58eDvqJbwLEQIcf2sX5X3HJsPO9yBror8Rpbv7OJ5CNjZZTvvuXCl/7gHp99zkWN14tjbJi8PpZuXCqZTWNCN5vPzira3sOuykIDOeEalxuNwS85YWqvsItEySPF4Ki2tCCsTthxrYVFqnij8tre1eNcKoxeF0q1FIX8dj7fQd7SQfkSIWCAYe4q9d0CWlNc0UV0fWsFFc00pFg0s16x2Q3YfaLl77ELkhw5bY/f1Ex8lp3zAiBKej88fHGu371pI+Xt+Q0hltTlg2HepLIGMi3BTaqFryeCl2OKlsaOXsYYP7RrhEx+nSvoE8c81E7njjW/Y6mslJstHSJqeM89LkAcXv3z5ddwNUWFxDq0bxacXfyLQ4NWJus5hobfcwNisB6IgADrEzOTdJN3lEixJh1KKtIww1a3hTaZ06yUekiAWCgYcQgIKjZkRKLMXVcjfk2CzR3KHr4m04CI/l6Ov3ukNnIiR3ht+T0GKTHx9PtO9by+Ft8nNdRSfbXfD4cPB0GG1XbpFnHqfK3eaKCXlbu8Sdb2xhX63cqHGso1eHG5xc9MwGapvbGZMRT5TJQFldKxcv3kBBZgLvddTyBd4ATcpOJNpspE3yYrOYGJ4cw87KJvLT4nj6molc8PR6QBZjy647jdmnyhE9RURKHq8q2LTk2M2MMB0GT4xOLCvNIOGi8JNzk1SxabOYmJyb1Bcfl0Ag6KcIASjokkExprDP5aXG8uGiaWq3b25y7MBL9wYyKEceB9eoNMn4YNubcObPe/d1joUnYXcYlANJw6Fun365bXBkTSn71vrFn4JXUqN9i17fHDISfSyjVzVNLTrz5V0BjSA7KxpD+u45W91c9GwhbZKXU5JiWHW7XBOr7QYekxGv7u+2175lx0NziLNFMSwlDmermx88vZ5DR/SfjwkP79kewbx0F2RMkI2/A0RguCi8NcrMlgdnixpAgWCAMsCv1IJIWP7VoZDLFfFnjTKTlxYvOnsVTGY4/xH9sri00OseLf3J389khiteCV6+YEOEtXzBUyskTMxbUsgFT68PW4YQbTb2afRK8ngpqmqiqKqJZ9bqxW1SjBmr2X/coeyNXG6J036/mv0dvn7761ooq21WxZkSLbxu8inqNj5g1Y7D6vaTHlkTJP4A8oyVJB7p6Dyu3CpHTLuBNcrMzPxUIf4EggGI+KsXdMn1Zw3lz6v3qo9X3jaFOKtZRPs6I/8C/5g2UzTkzT7eR3RsSBsjR6Iqt0JMCtzW4WEYCcPPBZPVHwVMH0+5IYMdFSVhN8lJsvGv26f1mYCRPF7mLd2gTtbIsVt0zz//kzMoyEpgw75aMuzWkDdBG0tqcQdkbrcfbKRgyCDdskvHZ/Cr93aqXbsXjU1Xt2+T9DMWM+NNXDU5l5/lDYEQmlsgEAi6wuDzaQZZhqGxsRG73U5DQwMJCQnH4rgE/Ywjza6gkVOCLohwnNhJx1F08UptLVRv/y8pWcMxp41GwsgPn17HXod/4kb2ICtLr5tEtMXU5zchJdVOznvi85DPjUq18a87zwn7+krN4p3LtwSNi9vZkd4NxNnqZtWOw1w0Np04WxTOVjc/fOYLygMM1ZVawjgLbM96HOPhrZ02zQgEgoFBd/SaEIACgaBfoDVJHpdlZ8WCKZhNRpytbiY9soY2yUu02cjmX80KKZ76Apdb4tLFhewNYb+y+u4ZasdvqO3mLilkT5V+u0XnDmf+jGERHb/LLTH+d6uDon+BvPrTicxMaQkW3P3BTkcgEBxTuqPXRP5OIBD0C8rrWlQPvO2HGnj7mwPsrmyksrFNFUGK396xQPJ4ueq5L9nrcDIyLY5Vt09jbIeR+bghdnKTY4O2cba6eWPTfi7umNYRyMXjMiMWr+uLaroUfzaLicnDU+UO60Dx99JsWDwJnpsuR6MFAoFAg7gtFAgE/QKtb50BuH/lTkDujh2bZVf98I6VzZBWkO6tcmKLMrEywFZFO5fX6XIz4eE1ne7z67I6AEakxnWaupY8Xh7/+HtA7vTNNjgo96Xiwd+R/+i8AuZNzApZ/yjVlmBWLHkc38EL58CtX4hIoEAgUBHfBgKBoNeQPF52VzawufwI6QlWZo5MCRIo8jqNbCk/wsRsOyNS46locJGdFMOKBVN4d/NB7luxQ11/V2UTq++egcloINNuPWbTZUIZKWttVbQp65GpsTQHdnoAI1NjsZhN7KpoxAA82DEruCAjnvcWTQv7HoodToqqWzDhYUXUQ4w3lrDNO4zL3b/Fg4mCzAQuGZcR0sJF8ni56o2DvOazYDN0zDN2fB+ZF6NAIBgwCAEoEAh6BcnjZe6SQnZpJlVYzQa2/vp8rFFm1U5l4eubKanxNzVYLUZc7V617m/uhEx+8+EudWpGQWaCmm4NVSPYV3RlpKyLEGqaVBRW3T6N/HS5RjBQ1O6sbArpFwjoRsZlGxyMN8pd0OONJfz3p9l4k4aTkRDNGX9YG3KMW3ldC/WHS7BFt/t3mjo6Mi9GgUAwYBA1gAKBoFcor2vRiT8Al+RjU2kdksfLZUsKufCZL3TiD8DVIfS2H2qg2OGkosHF1w/M4pUbTufjO6fz3sKpAHy2x6GrESwsrkHydF4jd7RovfoCyU6KIT8tWMANSbSx9cFZjMmyqx5/cydkYrP407cFmfHqdI+Saqf6PlxuiefXlajit9yXSsOgAgB8GRMZPrKAvLR4NpcfCRrjpj0ue0Ye27zD5O1SToWbPxPpX4FAoEN0AQsEgl5B8ni58On1FGk6ZqNMBrY/dD4H6luZ89S6kNsZkI2PAWwWI63tXvLT4nh/4VQ1cnjZkkJ2hpiBeywigZ3hckvMXVzIHoeTsVkJPHnVhLDWNC63xMaSWjLsNkakysJR8RgcmRrHW7dO5uw/fqYb92azGNnyq3OxOg/qunldbomJD3+iRgC/fuA8HE63vjaxppFsgwPz4GFC/AkEAwRhAyMQCI4LLrfExc+sp7imhezEaP595wzibFHsrmxUZ90qZMRbePCSMSx4fWvIfeWnxrHqzumU17WE9eIDWHvvzLDjzrQovnzQuyMLtY0g3dlnUVWTThRnJVqDpn18fOd0RmWE/s51uSW+KK7B5fby59V7KKttOe6CWCAQHF+6o9fEbaFAIOg1rFFmPr57pk4QudwSdyzfoq4zfLCNJdefrkbBxmaVqpM2FINjgD0Op7qfgsyEkBHAsVmRdQUHTvQYm5nAyoVTe0UodTZvN/AYOhOKh464dO+/IDNe/YxC7austoVFr2/BpbGK2X6oIWxtoUAgEGgRAlAgEPQquk7ZdjcLnn6HfbVx0GFhYjSZdDYoKxdMUSNzKXEWJj/6KW2SF5vFRKbditlk5J35Z/PDv3xGSV2b/sW6TmAAcn2iIv4AdlQ09lgo9STip00VK1G63ORYCjLi2dkxJWRslp03bjqTr/fXq2lis8kYNFFG230cSO7gmGNmkyMQCE5shAAUCAR9g0dCen42LzdvY1uU38KkyOFkd2UDtc3tqoWJMlGjpNqpRsBa2z1UNLgYlhJHRYMrWPwRLOTCpXmzk2JUL0GAsVkJPRJK4aaVhFpPOY5BMSYueqaQqibZwFobpXtv0bSg4z13VJp/R+0ueDwH2lvBYoP/LaP8iBRS/FmM8OGi3olqCgSCkx8hAAUCQd9QX4a1ehsgW5hkGxyU+jIAmLt4Ax5QLUzMJiPldS1k2q1qurcg0y/StJ58Y7MSAEOQMXRgs4g2zWs2GXWRxp7WAAZOKwkVRQxMNweSnxanHrPZZAw7Tg6QI3/tHV3T7a1QVkj2sHPJSbJRVufvpk6OMfHZL887ZiPyBALBiY8QgAKBoG8YlAOZE6FiCw2DCiivTFWfUvpcW9s9rPnewXPrSth+qIGCzASKHXJKtMjhxOWWiLNFBXnyAUFp2NKaZl2d4I6KRkprmlWBJXm8VBxpZXJuUo+jZKHMobVIHi+FxTVhxV9afBTvd6f2MGcqPosNQ3ur/DNH3vbpayYwd+lGdbXfXFoQciKIQCAQhEN0AQsEgr7DI0F9GZ9W2bjxH1tDrmLCLwgDx56dkmRjzb3nRCSYdh1q4KJnv9Aty7JHs+qOaVgtZp1titY4uTtoU7tDB9nUCSZKs4tS56dt5lAwAFsenEVirDXi13O5Jc783SrGe79jm/FUvvr1RZhNRi5b/IVaO6hQkJnAe73U2HLS03Feaq11AmstBYITEdEFLBAIji9tTti5EgrmQfIIzk6QsJoNuKTg+00PkDM4hgO1Tfw79rfke4rVsWf761rZc7iJMVn2Ll/S0eQKWnaooY0JD69hybUTg4yTZ+anBq0fiMstsWFfLanxUZhNJn759jZ2dKSn3ZKHvY5mxmXZeevWs5i7RBZ/AG2Sl1MGWdlf72J0Wgz/M2UYl4zL6DJFG9hgsmFfLY2SmfWMAy9s2FdLTnJskPgD2HkUjS0DCo8EL82Gii1yhPrnn4BXCqq1FCJQcLIjBKBAIOhd2pzw6BDABx/eDvcfxBodx9Zfn8/ne6v5zQe7qGzUN3QsvnYi9tb9DH2tGNDXDF7+10LeXzhN7YoN14V79rDB2CwmnZGywiP//h6r2Yiro7t4cm5Sl2/D5ZaY8LvVOpsVBW2qefuhBjaV1rGnym+APTI1liizCepdmM0Wrjx9aJeROWerm4sXF1JW26JG8zLsehGSYbeSGhdFlMmA26MX09mDbKIDOBQeCWrl84rBI+TIX0WHLVHFFvlx/f6gWkvyZh2PoxUIjhlCAAoEgt5l50r8sz188uNJP8EaZWbmyBQsAUJoeEoMozISMDNGrRn8zjCCcp8coWuTfFzw9Ho10nbVc1+G7MK1RpnZ8uBsNpXWEWMxcuXzm9TXqGhw8dEd03A0tamdx12xqbQupPgLJNpsZFJ2olobmJ8Wz9PXTFCNr3d04c2nzEieu+QL3B3adWdFI5/udnD2sCTyUmMpcjQzJjMBg8HA5X/dECT+AJZed5pI/yooKV77EHjpB3B4q7w8dQwMnQzGKPC65WiffYj8z2LzRwBzph7PoxcIjglCAAoEgt6lYJ4c+cMHGOTHyELn0sVfUF6vnwX81FUTO4SLUU7H1Zfhax2EZ8mXuvWUSFtnXbjWKDMz81ORPF5OTY/ju8NyVG5slp28tHhGZ3adSlaOdXBsFBYjtGs04JiMeO6aPZLH/7ObIodcC9gmealsbAtqUumsWUT7OuE8/W7+x2Y1opkzOAav1xs0TUU5voLMhLATQwYc2hRv0nCo2+d/zrFL/qfQ3goNByF5hJz2FTWAggGEEIACgaB3iY6D+w+qNYAug5VNexykJVjZ2yGatOhq90xmSB5BvsermiQrtYPjhtiZnJsUkbAym4x8cPv0Htm+SB4v85YUsiNg8kiU7GPNzf/YTEFGPHmpcerc43ve3MrKhVN1YlQrCMO9ttZWJhRKOrustiXoufy0ON6df7ZuBrAAqNrlT/FqxV8okvPlRhCQRd+wmcHNIQLBSYroAhYIBN1H2+QRHb7pwNnqZtIjazomexjJHRyjRuUU3p0/mUk5ybplnXXb9nT2bqSUVDs7nT2s8PJPT+dnr36jPl5770yyk2K6dWzhPANPSbKxv641zFbwh8sKuOqMrusKBxTtLij6BN66LvJtLlkKeOXz2GyFF2dD5RbImAg3fSJEoOCEozt6TXx7CASCyGh3QdEaaDosN3l8uEj+2eYMubrk8XLJ4kLNZA8vze7gBo1rnt+Es9WtPna5JS56Zj1znlrHvW9tU0fLKWLHjJdhhsOY6bo+LxIkj5eSatlzsKTaSabdytjM4C9OA3IKGGDcEDtThg9mXEd38rghdjLtVi5fuoHznvicy5duQPIEH5/yWspzZpORJ68ar1sn2mxk6XWTOj3mH45NE+IP/Odk6xG5i7c74g/gwwX+87hiiyz+QP655yPY+1/5/Hbslv95pN5+BwLBcUPc3ggEgq5pc8KfhoMUaLXigy+XwbS7gqIl5XUtlGpSlxYjIaNa7V64aHEha+6ZCaB66UGIOr9QFh5HEaXRevcp9Xbjsuy8Pf9sympbmP/PzWr61Qf8+crxRFtMaoTvrVvPYlNpHZNzk6hocHVan6iN9o3NsrOyo4ElNzlWTWvnDo6Rx7kZvIy2VLG3PRlPxwxlLdsONjIzf4DXqWnH5JmiwRM8KjByfLD8x/pFb10fvFr6eLj8ebmbWEQHBSc44hZSIBB0jkeCZdNDiL8OPn1YFmUB0ZHspBhdJC17cGzYl9hf20JpTTPldS2q+APIT4vX1/mFsvDoIZLHq/PuU+rtth9qoKLBRZTZGFR797/vbCfTbqW8rgVnq5u5iwv56Stfc9VzX5Jpt+oigtlJMbqIX2lNs5rq3XGoQU1xK1NO1t47k9X3zCQuyggvnc9HprtZEfUQJvRRU4sJJmUn9vh9nzRox+R52sAUxmPRYIaU/OBlgTQ7un7Nw9tg6eSQ57tAcKIhBKBAIAiNR5LTXsVrob4k+PmYFP//K7b4vdY6MJuMPH7FOPXxvupm8lJlETgyLbhusNXtIdNuJT9Vfi4/LY73F07RpzqV8XIg/1QK+HtAeV2Lzrsv2iy/jiLeMu1WbBZ99G1HRSOXPLue8574nEmPrNFFKisaXKqQW3HbFADmdaSEL3x6HaXVwQ0wCro0d4gZylraPfDjF78KmWIeUORMlS1bQP75iyK49m25sUPLzWvhprV6gegLJd4M/v8au5ipfJQ3HwJBf0AIQIFAEIxHkgvil06GN64EU0C6MTkf7twqF8srrLw1KCriDWgxe+LKCbJAmn82ZoP+uc1ltVz13JfscTg7xN9UrFFmfd2cySynfRdt7lH690izi8VrijjS7FLn+oIcadz8q1mqeDObjFQ0uEKaShdVy1FB7ai3aLORTLtVJ+S0Eb8iRzO3/HMz1g6ROTYrgdzkMBFRjchtGFSg+iFqUVLMAxqLVbZuuW6F/NOWCPnnw63rIGW0vE7GBEgbA+WbwOOvM8UYJadzlXUuehq/dyVwxcuQMqqTFzfJrycQnMCIIgaBQBBMfZm/IB7AE5D+Pf/3cvfvvGWySASo3CpvlzxCXa2yQV/z52hyMW5ouizoAsSh14daQ7enyqnW1l353JfsONRAQWYCT109QbZ00bxGpBxpdjHh4TUA/Hn1XrY+OCvIqkU7qi07KYaRaXHsrQrd5KKd99smealocHU5hs0leXnlhtOZnpcSvolDEbn1ZXxdacXz2jb1KaU7uDMLnAGFxRo8scNihVs+83v6mczyT7PVX8bgdcNlS+Vlg3LkUXD//T+/EXTeHMi/UI5qr7xVPrd1eOSa2PsPddoFLxD0Z0QEUCAQBBObDPah/sdp4/XpttwZ8v8Hj+g0JZuVaAv5ODspRk0HK7zx9QG1ZtBmMfHTV77m0iWFahRtZ0Ujc55aF7bDtiv+vqE86HFgh7EWs8nIM9dMDFpus5hYdfs03p1/NiM73kOgIHO2uvmmrI6RycGNGilx0V138Hb4IQ5J1ncjL71uki5KOeBRuoDbNTcoHgleuQBeu1z+6ZFkUfiz/+i3NcqfMSZzcDTRYpWXp46Cm9bA/MLQaeHN/+jLdycQ9CniG0QgEOhproHHToGGA/LjpBFwzv1wz279BRK6TMnmpcVT0CHqCjITyEuTbVTMJiMrb5uiSwMXVzfz5NUTePXGM9TUa6joW0/TnzPzkzt9HIoRqXGM7UgTj8mI55UbTufrB87j/hU7uHhxIdEWM6vvnsFbt5xFeV0LkseLs9XN2N+u5v6VO9lbE9w4c+frmyMWsCNS4yjosJ4pyIwnPz0+rGAdcChdwK9d3tEN3PFZh2sUSiuQ070g/xwcEEVWoomBU0BMZkgvgF8WQWBH9tqHZVskgeAERKSABQKBn3YXPBFQ+1RXDG9eI0f+tOJPoSNaFQqzych7C6cGmSNLHi+by4/o0sD5afHkJsfqbFHGZtlpdbdTXO0XfCNTY3uU/hyTlciYjHh2VTYxJiOeMVmJXW5jNhlZGZAmLql2qqlqJTqpnU985aQhdOauv6/OxZ7DTYzJ6nosndlk5L1F0/rU+PqEot3lT+1qu4DbW+XHebP8NZSKVZASlTaZ5WheTyd92BLhl3vl1K+C1AxP5MPdu+R5wgLBCYQQgAKBQMYjwfa3wdse+vn2Vtj3KYy6MPz2IS6uSppVQTv/1oBceh9tNvLu/LNUgaOtzSt2OHUzcJ++ZmKPhJDZZOT9HoipwONPjYsid3AMpbUtjBtiR/J4df5/Da1hPj8Nl/91A9t+PQdrVNdfwYGvf9LjkaB6NzRWyKUGyg2HzvcvCu7aId+UKHV7OVPl9TQ1lEFCr5OblYiITZZF5IsBdYd/GQ8PHBIzhAUnFEIACgQCvcGyKstCsPohuUA+MHqidA1HMEZLO/9WeZU2yUtlYxt5HU0YWtGjpGEVA2UljdwTuiOmQo2c0462izYb+fuNp3P9S1/rttsfQXq6TfKyqbSOmfnBHb4DGo8EL5wn++2BPuqs8/1zw1/GytYvBzd3WMJoxNfRCr3Oji8qDlLHgGOXf7lP8kcgBYIThAGeTxAIBIC+bkqRZaGK3mv3hPY/qy3Wj9EK8ATUonTXRoqShl1770x1ekZfo0QptWPdAkfbtUle/vnlAXZWNEa0zzEZcarXoM1iYnJuUp8d/wlLfZlf/IE/tQsdHb2ac9LjlsVfqLq93kbxxHzhPLnr3WiG//kAtSZQG4EUCE4QRARQIBDIqbKMCXq7C687eL3k/KMyX1a4d85Ibv3nt+pjEzAoxsTa76vIsFvJS4vHbDLqonDHMg2qjVJqm05KAyaD/Hn13s7ipSov/s8kzslPRfJ4VXubSNK/A45BObI/nzYCqAgrixXu+V5Tg2eAIZ3PTO4RHkm+gZHaoKkKcqbAqxdrbpCQjy8uDX5V4a9JFOlfwQmG+AYSnByEqT8TdIH2czvnV7Lps4IpSm+eC34rmEAGj/ALyFAdlh1o6/90hwGc/sin6uOCzATemX+2rrlihSb653JLfSqkFJPo7YcaGDfETqbdyoH6VvJS4yjSjKqDrsUfwF8+KeKc/FSsUWaR9u0Mk1me3BGqBhDkDnUVH7xyIdy63v83f7TfAx5Jru/T3gh1NmdY6RxW7GiEEBScQIgrpeDEpvUIfPk87P4QqrbLXX89mBAxINFe7BKGwM1r/EX1Zqvc2fi3i+SLsUIIs2cg4g5LbWStM3ZWNLJhX21QFG5YShwut8TEhz+htd2DzWJiy4Oze10EKvN5y+tayLRbVSE6NDG60+0sRmgP4fCys6KR0prmo6pf1HXAnswiQ7FdSS/QL/dIsHK+fpnjO79YjEuFD+6Qo3Pp4+Hy5+Ubke58F9SXBZs+K3OGPW7U+tiMif6bHG1zSrhOeYGgHyKukoITl9Yjsl+dFsX3K1CgKGkdrweMpu5fGE5Gaov9F7vGg7KdxS/3QcU2WWQ0HNSLP+h8/m4Xhfcut8Q+h5NoE7R5wGo28OYtZ/HLd7az16GfkzsmI54nVu9VH4/NSlCtXzaV1qk+ga3tnj5rplAaRrS2LweOhIkEIXcyv3HTmVy+7MteP5Y+ERnK3wScGH8PgdNpQBZ6L84GST9xhsPb5Fq9pOHyaLhIp3XYh8hj5Kq/9y/TRQB9shfmsJny56V0zmvtaLYuh9Ou7/+fp2DAI85QQf8nXFpnw5LgdTMmBAsUjwTPnwdV2/Tr3bRGfEkH8t2/4Iwb5P9r/dQyJsC853osFLRRO3WZ5KPa6eaDRdOoaHCRGhfFV2X1ZNitOF3tXPn8JnXdx380Tk3/Ts5NwmYxqRHAvm6myE6KIT8tjj1hRsIptEle7nt3R8jnOp39GwnhPO96irZrG06Mvwf7EP04N4CCa+CT+8NvU7cP/jQM7ivvWjC3u+CFc2TxlzIKLvsrOGsg+0x4cpQ/Mu5pk0fHeSV44Vw5CqmtBF11J2xaKgtPEQkU9GP68V+7YMCiTXV5JXhuhvxFHpjejUrofD+KcHQ368UfhE9lDiQGj5CbOmr2+JdlaUafdean1k02ltTqxJ/CTX/fTF5qLB8umoY1ysx5o9Pkmb3PfKFbT9v5a40ys+XB2cesmcJsMvL+wqlc+uwX7K1u7nTdooDndfOLNe+h2zWMOVNDe971lMBo2onw99BwUC/+ANb+uuvtpLauBXO7C/46Rf6eATnyHZ0AWadBTbFffEsuWH6t/DuwnwI1SoQ8oBK0Zg88NxNuK+zfolowoBFnpqB/oU11mW3g8/gbEbTpXY8EW/8evH3lVij5HDLHw7KZ0HQQbCnB66WP75Vu1hMak1mOUjw3Q75gpY+HtDHB63RTFEgeL6U1zXi8PkxGA7nJsaTGh4+EFDmauXTxF/z7zhmYTUb++eUB3fOpsZag6Jm2mSKUX1+44+quCbRWqP37rhmU1jTT0tbOlc99idvj03UAK6ltkC1f/nLNaUHCT9lnt2sYlVm1vVUDOChHrmPTRgD7+99D4DFDaNPyhCFySYOCObpzwdzmlNPFDZptUkf7Pw9tJFyhvVUj/pDTxlIb1Jf4l9Xs7v+iWjCgEQJQ0L8oXae52w6o60ka4f9Sri+D2r0EY5Bng2pprQ5e7bKl4s4cZCFx24Ze66CWPF7mLd2gjkgDOGVwDLGaob+jUmOob/VQ1eSvp9vraKa0ppnc5Fim5yXzZ039X5zNjOTxhhRtLrfE3MWF7HE4gzqFA49L6T7ubD3t+rsONXDlcxtxe3yqUFOaOLY/dD7vb63gvhX+lG+bJsDZ7PYydJAt5Gv0uIZR6TjtDUxm2az7RKoBVI65ahe8NEdOxZqtkJQHjh2QPEpu/Pjgdr8AVGoAwwnmdpdsKxMYWbzxY//noUTCq3fLZulKFHZwPhzeKovFmz+TsxWP5/pvWNMn9JqodrklNuyrVS2ShJ2QoDcQZ46g/+CRYM3v/Y/NNvB5O77oo+HWz+XlNcVyPZDWL0wlElMOZCNXgUwvTk0or2vRiT+A/QHeeSW1rbg9wb+nu9/cigHYUdHI8OQY9tXI25XUtDJ3SSGr7pgelEa94On1lHXsX9sprEXyeFm3tzpkR3EoJI+Xy5YU6gyeA4WaNcrM3AmZ/PXzferraymrbWHu4kLeXzSVigaXLup4rGsYw2IyQ+qortfrT5jMcnT/f0vguelQVyIvW7BJFrGBRtLXvN55A0hZYbD4A7/BtPZ10wv0UVijOfjG6f8OyDexcamyOO0FXG6Jcb/5D+6O7vJhyTEcqGuh3UufdcELBgbirBH0H2qL9bV68ZlQvw8GDYMrX5G/cJVxZSmjYNJN8HGgAAyDwQKJQ+UUjdbCQdCrZCfFqGPbQqHM0FUwGw1IXlkMagXXvpoWcpNiKO0wYN5T5dSJNsnj5dLFhTrxdUqSjbZ2jy5a6HJLXPLseoqq/etpO4pDUV7XEjTdI8pk0Ak1l1vi0sVf6F4/2mwgOTaaQw2yoNjjcDLtsU+paXaTOziGDxdNJc4W1fMaRuF16afpsCz+QE4JG83yZ6JN12ZG8Heura1U6KzGMjAKG3jjZLHC8PP831NKdPAoUvbri2pU8QdQUuM/51rbPWwsqeXcUWk93r9g4GLw+XxdhkwaGxux2+00NDSQkNBF4b1A0FMOfhM8ZF1L0jD/l34kzPg/SMkDSywMPzf0Hbug11FqAHdXNnL78q3q8vnTc7hh2ilM++PnSJpvHUUUjs2yg8/HjopGxg2x8/rPz+RHyzayp8rJuCF2VtzmT9uWVDs574nPQ76+0ngxdJCNS5cUsjege3f13TPUNFqomsDACKDFCF//ahaJsVb1vS147dsgQ2iFaLNRHRcXuHzbr+d0P1rjkeS051s/hSOlJ0bHbl+jnV0d2BzWXaGsNJ0NmRR6rnB3qSmGxZoJJSmjYH7Pm0H+s6OSW1/7NuzzH985nVEZ4roskOmOXhvA3yCCfsehLZ0/H078GczyMHaTVf7Sr9ktXxRm/jL4S1cUZPc5ZpMxpOHxsvVlLFtfpltms5j4cNFUHE63GpXTirJVd0wPKdIy7VaiTIaQqeSdFY3MeWodI1Pj2Bsg0vJS5WYSZ6uby/+6gb2OZsZmJrBy4VR1/2aTkfcWTqWoqonKBhdThg/GGmUOWd8YijbJS6LNzJFWKWh5tz0LPRK8MEuuNVOo3CpHy0+09G1v0lmHendLGrRRvd6osRyUI9cjKk0i1btloTr0jB7tLmtQmOk7QEa8hRGpx25EouDkQghAwfHHI0HlNvjovuDnEnNkI9baPcHPgVxnc+8e/527EuWzDxHRvuNMbnIsWQnRHGoMb568csEU4mxRxNmi1GXa2jzFjDmQA/Wh6wi17HU4daPbRqTEEG02Meepdboo3Y6KRr6raCTOalaFptlkZHSmndGZdkCOCn62x9Gl+ANZZBY5gu1iokwGJmUnht8wlDFzbbFe/An89GLtaq9iMsMZt8BH9/iXvXwBPHCoR5FFg8EQ9rkl1/XBLGTBgCEyLwSBIBweSU55eKSu1w23/Quz4MXzkCfCBnCkDOb8Jnh5yii4ejnctx9sifKdu8XqrwN65QI5DfPS7J4f28nI0f6+uoHZZGTVndMId/mymg3kDA5fi9cZHm9o8ffri0frHj977URW3z2D1XfP4K/Xn66mdQNTtJcuKeS8Jz7n8qUbkDz65ySPl3lLCrnp75sjOra2EH6HZiO4PT5+/OJXQfuX31CHMfPSyfK/F86TU5Pv3hy8ri1J1LD2dyZcrX/sk+Q0czeRPF7uXK7PjAxJtJIRbwHg8mVfMi/EOSsQRIIQgIKeo9ThaIVWdwVGfVnnEY6MiTD8PHzJAemuK1+F0ReGvqOuL/N7dinegQOddhfs/giWTe0TYSx5vJRUO3UXIsnjpa5FYsuDs3j40jGkd1y0FFySj4qGEB2YEWAyhpaVb3zl9xAsyIxnRGoceWnx5KXFk5scy7gsOaIXbQ791ad0CGspr2thR0BTiHYfOUk2xmQmqMvK6/XvKS0+CkVvhto/EGzMfHgb7Fsrz7cO5Jo3RFS7vxMdJ49VNHac8z007y6va6E4wFz8itOGUNnk9z/cEe6cEgi6QAhAgR+tePNI4NgNh3fB4Z3yT8duvcirLdYLrapdsOTM7gnCQTmyX1YgyR2F0zd9gmSM4keeR/neOwQAX1ddvEonIHQ+u3ag0O6SZyYvv8Y/27cXhbHisXfeE58z54nPcba6cbklLnp6Pec98Tn/8/I3XDs5m5dumKzbbmRaXKfduJ0xdJBNrefTom3M+POVE9hd2cA/NpSx82A9ACsWTGHtvTPZ/KtZ5IWonQrVIZydFMOp6f51DcCz10xQo4hlda385eoJvHLD6UGRxWizkaomNzaLCYBxQ+yh37NSN6bFGyqqY4LMCfJ/211QtEb+Keh/xCbD/Qfl2cE9nN2cnRSj3lwo3DD1FMZqlo3NCnNOCQRdIG4jBTLarrqMibL/XpDHHvJFymyVo3YZE+R/lVvlkWIvnCenOkDez761sPb38n7CdS6azHDzGlmYHDkACZmy55/GmLa82sm3la1czKNkGxy8dNkVDOssAtKLI8xOCkrWBXudpZ7aa8K4vK5F9dgrrWth0iNryB5kVa1XlKhXYNRu4Tl6Ea/tylX2G2pqh+TxctVzX4ass8tJslFW18rYrATufWsruyqb1OfGZCbw/sKpak3hR7efzf6iXZR6Unjq01J2VTTSJnmRPF6d0S5AQ6s/4uIDnli9l7GZCWrHcqg5v7+fO4b/9/4uQLbrePXGM5g6Ijm0AbXJDFe8DMum+Jcl5coWSNrpEnigdp/cEPXuTeBxydGlHgoMQR/TXfPugA5mZQzh7soGth9s5NLxGcTZoli5cCqlNfL5H2rajEAQCQP8yjiAUL5Y4tOhdD3gk/2qQK5NiU/3R/MqO+nG1Y4/qtzqj1rUBDRpmKLg9Sv164brXFRMVtMLQr5kdlIM47LsbD/UQHzWKLKTI7A86K8F4scDe5b+8aBhcPOn3RfGoZoUPBLZvgqGD4pmX73c7NEmeXW+e/kdkT7lgqVw55tbeX59Ce8vlFNjyqSOsZkJYDCwI8zUDq3gDOTpayYQb4vC4/Ux56l1uud2VTT6vQQ9EuZXzmd4xRayUsZza8UvABN7q5xcsvgLDtS14pK8WM1GYqJM1LXoR46V1baw+u4ZmIwGVaTmJseqonBsVgJXTBrCW98clKePDLGHF38KKfn+UWcZE+XH89frJ1WkjZXrZbWCvr2161m3gv5PGGsbs8lIwZBBFAwZpK4artNeIOgOQgAOBLRfLFrMNvB6wOsGY5Qc2Qvlih+O1NHg+D7Ma7p7frwBmE1GViyY0u05roIOUvL9kdqUUXDL592PFnkk2aOxcqv8OGMi/OxjeOUCzBVb+DhtPKPqf4EHOdU5JiOeXZVN5KfF8/7CKapAykq0ceiI33RXEWWAKuq09XahpnZobwgKMuNpbfewr0Nw/mrlTp66ZiJDB8l1ebs0+xqTqUnvaupErdXbyDY4KPVlAOgiiy7JiyuEp5/NYmLoIJvO089sMrJy4VTdedqt81YZdaaNXJs6asmWTZcjge7m4L9Rs7VH9WWCfkZA7fKBkl1kDBsrvu8EfYYQgAMB7ReLFu2sXa8bIm0ky5gI85bJF6mlZwekqDrZ5ig6F8PZgQg6aHPCzpVQMC949JXJLKffjyYlXl/mF38AlVuQStdj7jivLFV6EfXEVROIMht1wkdOZ53F6Y98qu7m1Ix43JIXt+TFgJxeNQCj0+P47rAzZE1eoLAqrWlWo307K5uY89Q6xmbZeXf+2RQ7mti8/wgZdhszRmoicINy8GVMxFC5hW3eYZT7/N58JjycYnCw35eqCtpAWts9VDS4gs7JwPO02+dtqMh102H/31h9if5GbVAuzP9CpH9PBjRTTPaa8rjwpf2k2atZdv3pnJqZIISgoNcRAnAgoDRaBHbbKgbKkWKKhh+9DEk5spiTXNDVIJnEHPjxmyfGsPkTlTYnPDoE8MGHt8N9ZbI1jpajTYkPyvFHEQFfxgSu+sjEQ95hjDeWBIkoyeOhqtFFpt2qu3DVOPXnm1vycMHT63XLfICzTbFSMajTN8Bf76QVVkMH2chPjWOPpgFkx6EGSmuaGZVh54GVu+Q0rDadbDLz3Q/fZdHSlZR3CL2h9igqGlpZEfUQ440lVMefylnV96sicHR6HGajUa37O2aF94HjzX76L9i/Qa6XTRkl/q5OFjpql8uKd3HhK/vxYKKioY1LlxQyPDmGVXdMFzN/Bb2KGAU3UNj7X31NHsC178Cnv4PD22Vx5wlh2Js0HOr2BS9PGwe1e7tOGd+yTh7eLug7vv4brLrT/zh+CNy1rfeFgaYGsMSXznlPFWLCQ7bBoYoogOEpsRyql2volGH1ZpOR0ppmbvvn5iBbi67IS4lRawrHZtlZqakJVDqQtx9qIGdwjG42b06Sjb9eP4kLn/lCXbb23pmqcPzHxjIe7GjSAHjo4lFs+uYblh25RV1Wd+MXLN7i4fLcdkadOh6M5uNTiiDmAB8/jvFnX1TVFFS/CnLX/L/vmC4igYJO6Y5eE2fSQCF3htyYoeWNK6C6o3kjeRSkdJjomqPln0nD4Of/lbsMA6naHl78JeXJP9MnQNqYoz50QRdkTdQ/bjoIVTt7/3VMZrmJJ1VuxBmXZceDCU9iri5VesnYDLVurrXdw/PrSrhsSSFznlqnE38jUmIoyOz6hlLbULLjUAPFDief73Hgcku6hpCy2hZyNGOzyupauenVrxmTIRfLB0btTsv2F9UDnJmbzOKF83ClyDcsrpTx/Hylg7nf3kjBytmYXj4fM16GpcQd+4uwEsEV4u/YEsrrtI8ZOsjGiJTgrvK9VU7h9yfoVYQAHChYrPCLIjntq0WJ+lVtgyv/Bos2wy9LZJuQuhL428Vyl2Egg/Ig3IyHy5fJ+7l5gA+sP1ak5Af/Xt++sU8vVkod3sd3Tsfh9Df8WM0Gbph6ClaN0fKTnxSpEzi0PHvtaby3cCqrbp8W1pg5kDEZ8cxbuoGfvvI1Ex/+hNS4KNXcedwQO/+6Y5puusihhjaKq5v5+M7prLhN302cnx7PmA5/vzEZceSnx2O2RGG+5RPmJz7PmAO/4EhVKeONcv2doVKYig84jrGpvGJxVFzdzNBBVp15erf8/tqcsPkf8k+BIAzi6jyQsCXKEb0Xzwt+LnOiv07PsRsc38nLq0N0+Q4aAZ5W5GqtELy3EG4rFOLvWNFwMLiWs75Evlj1oRWO2WSkqtGFq93fPbT4x6cRZ41iRGpcSNGnMDJNntBhNhkZk2Vn869m8eH2Sl5cX8K+mpagdZ+5ZiImo4GD9S3c+LdvADm6+MG2St669SwqGlzqxTEuSt+40SZ5qWp0MSpDH200m4y8f/v0oJRu+RE3Hx+WhWG5L5VtHXWOvoyJGAa6qfhAI7D+shu/f62vZaQRY21E+0C9i5FpcfztZ2dhMhoi9/sLrAm+/2BwY5hAgIgADjwyxsumzVpMUXJheYevG+/c1Pk+Gsuh8WD452t2+ydOCPqe+PTgZenjj8roOdRot1BMzk1Sp1zYLCamjUimvK4lpPhTvmyizUZWzD9bV8f34xe/4v6VO7FFmRmR7I9yWEwGXr/pDKLMso3M2cMG66KLD7y3kyuXbVQvsuV1LezUmD8rx6UYOgeiNJNoL6yKzQzA6MxB1F39L9pu3YThpk/ETc1AQzGVX7RZ9eWLBO10nFDzpRVcbkktZwD53MvXTKjZW+UkymxUb5YiYudK/Dfnvo7HAkEwQgAONExmuHUd2If4l3ncclehR4KSz8GxI/z29lMi8/h79+fHpF5GAJSsD152zv09FiuHG5xM/sMnXV68AKxRZr5+4Dweu3wsXz9wHtYoM9lJMbpRVaBYq1RiwkOb5MXR2KKOCNRGPXZWNHLfhaPV7do9PqY+9rk8Zu7Jz5E8Xt6Zf5Zu3zsqGimtaaaoqgm35FVfuyAznlduOJ0tD87uVvekkt5effcMDMCN/9jKle/UIB3N12W7S27EOrxT/F30V9qc8M3foGKr/nfUg/pL7Tkdbv6zyy0x8eFP+OkrXzPut//lSLNLjkovmkp+miwCe9RtXjAPf3mOoeOxQBCMuJ0diFiscPVr8PxM/7K41NBm0YEYjWCyyiOoOqN6d5+nIAUdhCrF/PyPkDen2yKwpqmFsx79XH0cyohZi+Txcs0Lm9hZ0cg/vtzPex1TPe45f6SaqjXhUa1VtnmH8YD9T+SunKuOE8z+2WrV2HncEDtDAy54ynzd0lp5zNy788/WPZ+XEsNdb25VTZ8LMhNYffcMdTybNg2npOUy7VY1bRwqsmI2GTEZDaopdVefQ6e0u+DxHH8tbfoEUR/b39CmTUEuczl7AYy/ukfpU61ZeTgRt6m0jtZ22e7I7fFxxh/WsuOh87FGmVl1x3T1PO0sjexsdfPh9krGDUmUa1hNRvl47z8Y3hdUIOhAfAMNVNIK5DTh4W3yT6O5a/EHUF8KsWnQ3IUA7Ga9jOAoGH6u3Kmtbdap3IpUW4I5dWS3drVkrd7Ue3CspdMIRLHDqaZ7d1Y0sudwE/ev2MH2Qw2Y8TDU4MCIR22kGG8sYeUF7Rje3qoep7m+RGfsDFCQEa+mcqNMBtwe+cLcJnlxNLWpI9dGpsbx5FXjuHjxBvWYdlY0qnOH5y0pZEdFIwUZ8fz5qgn84u1t7KxoxGYx0truVb0BtbN/lWhhJBfxiCgr1P9uDm8VN0f9DV3aFKgvhn/fI/+7/1DEIkpb99fVFJjJuUm6c7vd42NTaR0z81Mxm2QTdcXiaERyDDdMy+Wy8ZnE2aKQPF52VzZyyeJC9ajHpMfx/u3T/SJw0k+O8kMRnOwIAThQMZnh5rXyhcg+BF6+oPP1jRbwtgMGaK4K2Fe0bP1SvRPSxsOPnhfGz8cSixX+twxK1+Fb+3sMh7exzTuMh5ZX8s7CERHXDkkeL1+W1OiWrbpjSqfbVzboO8S3HzzC9kMNmPDwbkfUb7s3l23eXMYbS2lNGYdl0JCg/QROzHjntilcuvgL9jqaGZEaR7HDidvjw2YxMWX4YGaMnKqbBKIlLyUGj9dHscOpRvB2VjbpDKdbOxpXtnfYysxbuoHWdo/qW2iNMvfeCMKcqXqBnj5B3Bz1NwrmyQ0ToRrbdq6MSExJHi/zlm5gx6EGUuOj+PcdUzuNGFujzHz1wHmc8Ye1tHec29paVW0aubimhf/33i7+33u7uHlqDoUltXwXUOu667Cz51FqwYBEXKEHMkptS02xPIC+M7ztYB8KDQeCn/O0wZUvyVFEYVR7fLBYYeT5lNrP5Od/eUc2Zq7o3gWhvK6F76v8YmrF/LNJt3e+7dnDBqvRNJvFyCXjMnjjqwM0VexWo37jjKVs+MEqbt10iE8O2Bi5vJpV6RMwHt4adkRgRYOLvR0zeb+rbOKjO6bhaGrTReiU95WbHKtGBPNSY4k2m5jz1DryUrt+3+OG2KlsaFVTca3tHjUKA700glAj0MX0jn6Kkjbd9iZs+ivUFvmfi7CGrrSmmR0dgs3R5Ob0Rz5l64OzSIwNP6YvMdbKjofO10WftWUKgRNuAF4oLAu5rxEpMcduOo3gpEB8Cwn0VgeBqUQtDQfCTAwxyFFEUWty3MlOTiA+cxSegLRlJJYUgSnPcUMTu3w9uQlkFqt2HOaisenE2aJYsWAKpY4x7H1xGSM9Rew15dESl8N/DssXx+8drVyU8hDv3JzBN412Jrm9OJxO9VjL61pIjYsiPy2OPVVOxg2xk5cWz+hMe8hjMJuMrFwoRwTdkleN9BVpLpyKSLWajbgkLwWZCTx19QRyk2ORPPLEEiUCGK5j+KjoEOiCfkx0HJz5c5j0U6jaBYe2wLgrjup77e8by7ljdudlGNYos3rDoZ1sMy7Lzru3nc1lSwsprg5tAB1tNtAm+chLjeXDRdPElBBBtxCj4AQyyrgj+xD557s3y9M+tKSOhdY6aDoUvP11KyBv1tG9fseYMZE+PjoCxZ7k8XLZ4i/YWdlEXkoMH94efqZod73LAi9YK7Rj2trdHCz5njs+qmP74eALWLTZSJvkxYCceCvITMAtedjraFYFWX5qHO/edjYOp7vLY3K5JS55dr1ucojCx3dOJ8psDNv84XJLQTWAAkF3kDxezn/iU0rq/PXRHyycGtGNlEJJtZPznvA3Ya29dybZSTFs2lfLdS9/FbT+qtunYYsykWA1svyrQ1x/1tBOI46Ckx8xCk7QfZR0sMUqj/u65VO4+Gn9Oq760OLPYpPrnHqKR4IXZ8HSyfK/F4/NyKWTlUBvu2KHU22oKKpu4ZJn1oe1dgnli9cZgXYXpTXNqn+g2RKFN2l4SPGXOzhG7e5V7kB3VjSqaV8lJbvH4eRHyzaGtaSRPF6KqprYXdnIpUsKQ4q/kamxmIwGspNisEaZQ74/JQojxJ+g2zTXwEf3Y3bV8cHt04k2yQ1IVrORkWndix5qPSiVCL7ZZEQKE6eJMhtJijFz+iOf8ufVe5nw8BqONLvk788OmyWBIBzi204QGpMZxl8DH9/nn/nbeFAeEef4Tq4HvOkTOLyro8j9KO4668ugcqv/sTJyS3RJ9gqBjRpFNS29ViyeGqefL33Dy19yqMFNQUY8T10zkaGDbGp9HqCmXgfFmDj9kU+73L+SBoZgKxZt0b2CCQ/ZBodcA4mJkWlxRJmMzHlqXVCEMhySx6s2lkQ8fUFwUhFxJLzpMDzRYay/aSmOG7fR1tHV65K8VDS4uvV3Fq7xaHJuEhYjaIbuMMQeRW5yLMs+26fbx2sbS1lYcpt/ekk3DKwFAwtxVgjCY7HCL/fBc9PlucCZE+HGj+XRY0qzR6gpFN1lUA5kTPCLwAxhIdObaBs1QDZI7q1i8a/K6nWPDzXIJuE7K5tU0fX2/LM5UC+L0EExJl7/8iDvb6/oct9/uKyAy0/L4qrnvgxpxVJe1xIk/rR+g/fEPM4z10xUawIj8TQsrWnm7je3qtY2YzMTWLlwqhCBAwhtWUN+WhzvL5waOjLskeCv+sxH9q5ljMu6+KisgwIbjySPLCR/NiWH574oU5fPHT8Es8nI9WcN5c+r96rLR0fXBc8vFjfTghAIASjonOg4WPi1/CWiiL7e/jIxmeGmNaIGsI+wRpnZ8uAcNpbUkmG3MSI18hRvIIHRsQx755Hf7Yca2FRax9QRyThdbiY8vKbT9ZV6QIDXvyrnqjOGBkVEtF2SeamxFHWkjbMNDp3foLdhPybjWWpjy9gsO25JThkHRva0F30teypqqP72AzKycsEcLc7NAYC2rGFPlZO5iwtZdef04L+Z+jJo0dsmmWf+ghU/SIq4jraraLP2vByREqt77vwC+eY7MdbK1gdn8eL6Mp5bt4+bV9WyIkqeX92SPI6vHDbOSpBEeYMgCHFGCLqmL0RfqNdIHdW3rzGAsUaZOXdU2lHtQ/J4VWNlgLFZdt6+9SzGZMSzK8CTzGo24JJkb7OfvvI147LszB6V2un+/7VoCmDg4sWFgFwTqETstGlf5YJYkJmA1+NRt49PH4EnegKmw1vZ5h1GfPpwDta38PpNZ1LZ2Mbdb25Vo4GBkT3tRV8hCjfbrbdiXaXpek8fL/tnChF40pJpt6oNSiDXoZbWNJOXFq9fUeueEJMMtxVCbDJmiCjtG+rvaWVAiYLOC7C6meHJMeyraeHU9DjGZPm74uOsUazaUdmRIjZxufu3cinEwVQ8f9+K1Wxg5YKp3ZspLDjpEd9iAoEgIsrrWtSLFcCOQw1UNLh4f9E0iqqa2F/bggE4JTmWnMExbCqt46evfA3IkcCH557Kk2uKwuwdYqItXU7fCJwbrOXxq07DlLoGqbaE6PZkipZ9xY1/+wabxcTKBVN06+/QiEvQW+CMzbLzpyvGEXdwnV78gTw5p7ZY3KycxFQ0uFTxp3D38i28F2izYjLL9XXa7Eg3CPX3VFrTrDYsKdNAtH8Pr//8TDaXH2FyblKQUCyt9TdAeTBR6stQH7skHxc+80XEdbCCgYEQgAKBICIy7VbVRw/khg7lQjU60x7k0zd1RLLu4jUmK5GtD87in18e4IrTM/iflzezt6PBoyAznrZ2D6U1zbx161lh5/Rm2q26xhAtPp8PTGbMqSOp3F2lM3eubGhlbJZdrRnMskeTFOP/+gtZfJ84DVb13ucnODHITophZFqcem6CXNMasn70KLIj2UkxugapEalx/OyVTRw40qaza1LOy0y71V8PGyDkspNigv4uRqTEBPkHHtVMa8FJh7gNEAiOAWW1jVz7/Abe2rQfl/vEtGaoaHCp4g/gqasndBpJUETV2ntnsuI2+WKVGGtl0aw80u1x/PuO6ay+ewYf3zkdMHDhM18w56l1XPHXDWrkT7GUATlldtVzX7KnyklqnCXk6ynr/fk/e9TlBuCMUwaxcsEUPlg4FQNwqKGNiQ+vwdnq1m2vs4g5uDn4TaWMCjm5RHByYemYJa1gs8gekr3Nk1dPYNXt0xiREkuxw8mBI3LEWWvXpJyXFQ0uneVSeZ1f3JlNRt5fOJX8juk344bYeW/BVPJSYtTjV5aLaSECBREBFAj6mLLaRs75k1x7trGknvtX7mTX735wwhVlB6ajhg6yUVLt7LTYvbNRamaTkby0eIqqmnTp2Z2VTew53MTdb25hr6NZrdfTpn8dznbdvvJSYhg6yAbI6TBtTaIPqGxsIy8til0VDWqTiQ/403/3cO2Zp4RujMmZCmar3wYpZRTc8rmo/zvJKa1pDqppbW33srGklul5KZhNxqMyDpc8XoodTu5YvoW9VU7yUmMprm4OWi/Qrqmr8ghrlJlVd05Xo9jldS2qL2Zru5dXbzyDqSOSRfpXoCK+yQQnL8p0k+M8n/hX7+7SPfaAbt7sicRjPxpLZYOLM3MGceWyjeyoaGRkaiwfLJqmuxBG6qPmckssfP3boOULXvuW/R0Rjh0VjZTWNJObHEvu4BhdrRNAlMlAUXULc5cU8v7CqSFTeArjhiTqHr+6sZxXN5ZTkBEfXONlscJ9+6FkHdizICVfiL+TFEXQTcpO5J63toVc58a/fcPYLDv/+NnpTH70U9okeYTglgdnRywCJY+Xy5YU6m54lC72QPICZvuG8wjUor3hChSMQvwJAhGj4AQnH8pYuRW3yEX76RPg5jXH7eKtjQACmCCyCGA/EbDQMWZt8RfqxSovJUY3dWNkSiz/vmsGkkeOlDzx373srGjs1EdN8ni56On1QcPutdYuCh/dMY3RmXacrW4mPbImqEhfIT81jlV3TkfyeLl0cSF7HU5dd6Xk8TL32fXsOhwsDtfeO1PURg1AXG6JiQ9/Qmu7R9f9G460+GiqmvzNQa/eeEaXN3PKDZF2VnVnDE+2seqOGQBHNaKwu6MdBSc+3dFr4nZWcHLR7oLnz4Hq7/3LDm+F6t1yCk+Zd6w1s+5jcgYn8Nkvp3P/uzuZNz6LSydmdf1l3u6CF84Bx/eQNBxuXXdUQ+mPBpdb4oKn11OmibwFjlzbW91MscPJvKUb1OYLCO2jpnifHahr0Yk/A/DhoqkYDAYuevYL3f5/8dZW3l0wFYfTzYrbpgQ9r76ew6mmzf6tSYcpr202GXn/9ukdx1rYJ+bYghOLL4pr1HO2TfIyJNHKwSOukOtGmQw68RdtNjI5N0m3TqhZ3Ip10fDkyM6xmCgLksfLGX9YS2u7p9uRRoXOSjAEAiEABSc+7S45TRefBu/eBLV7g9cp/QLeXySPmVPqujImyuPsjpEIfOOWKZGt7JHghXNl8QdQtw/+NAzuKz+6kXs9QPJ4mbu4UCf+QI4ARlnM7FI9zBKobGjViT+FPQ4nOw42MPGUQUHj28xGUAIuPuBgfStPrt4TtI9dh51c8sx6impaGJtlV7sno0wG3B5/EkMRcp1FPswmI6MyEnrNHFtw4uJyS9z+xhb1sdVs5F+3T+XK5zZRFBCZTo2z6GpPs+zR/OeuGUGlD4rYUzp1tbWr+2qCZ1WHYkdFI6t2HNZ1sp+oZSOC/osQgIITmzYnPJ4LHnfn6/3nPv//laL+yi1QtQsyx/fd8XUXjwQln8vzlrVIbVBWCHmzjunhlAdE6UAWbW/cMpnEGKtuioHkkWui5IiFkbRYM2VH5N/LvL9u4JtfnUujy6sb3xaYbXv0o91q7V8gRR0Xzx2HGlh99wxMRgPFVU5u+ae/W/fe8+W5rIEX4VDirjfMsQUnLi63xPPrStQoMMDiH08kMdbKR3dOZ9ehI1yxbCPtXjnSt2Lh2Ux7bJ26rt1mCYrIacXe9g5fP0C9YTkl0cL+I/oGJoVfXTiK97ceYmdlE+OG2LlobDq/+fA7NQIYGGkUCI4WccsrOHHxSPDcjK7FX2e8NFsWkf2BNicsORNeu1yOUuowQPbkY35IileZFskLUx/7HMnjJS8tXp0uII+cm82rN57BlgfncO7oDN12Sz8tlfenmWCQl6ofb6UVf3mpsVjN8leUzWKkIEOexDA2Sz6eTLuVp9f4o70GYGxWfNBFuDyMoBQMXFxuiQm/+y9PfuI3JrdZjEwbkQzIUeLx2Uns+M0PePXGM9j26zkUFunnXn932KmeW5LHy+7KRkqqnbrz9J63tjHnqXVgMLDytilhxR/AIx/txmA0svruGay4bQpxtijN31P3078CQVeIJhDBiUtNMSyedPT7SRoOC786po0WR5pd/H1jOefkp3JqZgJmVx08kQ/eTjwCF20+LkPddx1qCFlz11Xxe01TC6c/8qn6+JtfnUtyfIxu/unQQTa1m1gZHwcwMi2ODxZOBfxF8GaTkdKaZu55axs7DjWENIS2mAx8/cB5/M/L36jdj4oHoeAkpN0lR8ZzpnarPOJf2ypYpEn9Anx853RGZYS/vjlb3RT8drX6eExmAu93nKNzF3+hWsfYLEZWLpiKyWiQxV8HgSnkcIhmJMHRIJpABAMD+xAw20BqPbr91O2DfWshIROMZrk5pA+bRI40u5jw8BoAnvykiDMHtfFm640YOtsoY6J8PMcYZ6ubW/7+TdDySFJSyfExfPOrc1n6aSkLzs0lOV4ugFf8/xRWLpxKYXGNOjYOYMmPT1MjHlqRaTIa1BTynionI5JtFNf4f//tHh/zlm7kozunh50mIjhJaHfB4znQ3ipHzG/8CJqqIHGo3PDVyd/u16W1useDYsyMSO1cdMXZotj50Bw+2FbJuCEJjMqwYzYZKapq0vkGtrZ7qWp0MSk7UbUtGpkax15H6EzDivln8dAH37GjolEYNQuOKUIACk5cGg52Lf4ufwU+/yPUBjcWYB8KDQfAYoPXr/Qvt9jki0rmRHnWZy+LwH9+eUD9v41Wlrf8HEMo9ZcxAeYukUXp4BHH3ArG2ermtN9/omuyGJESwwM/PJUpwwdHlJJKjo/h15eOCfmctlEjcGxcbnJs0LqlNc1IHq9aT1WQmUBxiItqaW0LFQ0uEUU52SkrlP9OQa7rfeFc/3NdWD/dPms4r35Zrj7+8PbIosRxtih+fNYpna4TbTYyJjNOtSuKMhkwh9n1ytumMPGUQarRubhhERxLxJkmOLHwSHLq1yPJEbG0Tho4froKvnxGFn/Jo2Bwvv+5jImw4Eu4ZLH/IqKgPK7YItvG9DLXnzVU/f8lxg0YQ4m/i56Gm9ZAegGkdh7N6Askj5dLni3UiT8Aq8XMjJEpfvHX7oKiNfLPbu7/8qUbOO+Jz7l86QaAoLFx2nXnLSlkzlPruPCZL/ABq++ewYJzhutG0ylkJVrxeH3qCDnBSUrOVPlmLRSHt+r+diWPVzdWMDk+hi/um0GiTT6PF/xzW4/Pl9zkWLVONifJRuF9M7lsyZeqn6Db4+O7EL6TALYoExBiDKFAcAwQEUDBiYNHkps2Krb4o3O3rIXKrfDW/0DjIf367S3yugA1u/XPzV0Mr14sP2+0gFdTm2OOlrtuM/sm7ZoYa2Xrg7P4+8Zyzk9KhA9eCl5p6OnH1fy5vK6F0hDNEzsrGv3jqbQpOIsN/res6zqsDnPrck9yUKPGsJS4kFG78roWdmhHxXX8/7F/fx+0rgGwW83MeWod+alxvL8otAm1IDTaiRgOp7t/R6QsVvmcK1kHnz4Mh7f7n0sfL9fTeiQkjGpXeEFGPHfNGUl6gpW7l2/hSKtcc6s9B7uL2WRUI3iZditzFxdy8Ij/ptJkAE+YSntTyLs/geDYIL4ZBScO9WV+QadE55JHwJDT4c7tUL0H3vmZLPYyJkLuDFnEVWyR06kYZOuXzIlyWlXZlzegMDshG675Z5+mXRNjrdwxeyR4hsG6XDhSqnkyV65h6iV6Mg1AO0YqJ8lGbLSZXR32FGqNkjYF197atU2NRsDnZEwkzvJLnO1yPWGmPbxwVDqRd6ieg3IX8f4As95MezTLrj+dS5cUArL/4Nwlhay6Y3r/FTH9CO1EDIWQI/L6ExYr5J8PI86T//6PlMu1vO8vgqWTIX085fM+UG82dlY2cdPfNwftZmRq3FHV3ikRvJJqp842yWIy0O7xYbMYaW33UtARKdxZ0cjYrOBSB4HgWCIEoODEYVCOX9AFRudMZkgfA7cV6sen3fixv0vQaPZPAqkvg+R8qAlRG1hXJEcPjkUEzmSG276APw2X65jMVvmxV/I3pihisHo3NFbIwraLSJtSM9fS1s6dy7dSVtfaqSdeIIFzR4FgEamk4JQIYM7UTo/FcqSEnA7RbazcQopUiZMMWts9as1eKLGqRFi0noOAThQCVDS0YYsykZ8ap16E91Q5exzZGWhsKq0LMvLeWdnEnsNNjNFY9/Q72l2w71PZSXzEufIYyKqOaODhbWR7ypmQGUdDZRHlvlQ8mIJ28csf5Actg+7fPGlvnHIHx1JaK5+zre1eXr3xDKZ22MyIej9Bf0DYwAhOLCKZj6udDPLhHXKKWEkZgz+NHJsBzZWh9xFpSrO30NpZgD+1CnLtojnaf1Hr4tiUmjmtOFLodYuJLmw4tMdiwsNHsb9jpKcIT/oE8svuRcKEAdjy4Cyqne3c8+ZWuRsyArG6u7JRN1d1ZGos/75Tnkc8d0khe6qcwgYmQiSPN2g8nkL2IBsv3XAGGQnRbC4/0uO5tL2OR5Jvil6c7W8GM0bBFa/AW9f517tmOb7PH8NQuYVt3mFc7v6tTgQq83+Vcw6gtKaZxtY2bn9jKxUNbbp50l2hiMZMu5Wrnvuy9+yI+tFscEH/RdjACE5eTObOvfDaXfDHbPC06ZdXbJEjA9rUbzjxB5GlNHsTi9X/WkVr9I0pgR3MXRxbYM2cQu7g2N63mNAedxfH4sHEhc2/5rOfn0KJlIJUJv8efMC8pRsp1Yybi6QmK7B+6ulrJmI2GTGbjKy6I3gOsCAYJTqrCO9oU3BNWnl9K3OeWocB+XfV07m0vYq2HliL1y1Hz7WR6cShGCrl9cYbS8g2OCj1ZZAeZ8ZoNFHRKH9XKJM7lM9Cy46O57T2ReHQzt/VRtGPWvx1vN/GpLFE3fIJVuuxHQspOPkQ34yCk4uSdcHiT2HlrXL6N3Oi/Dh9vOwjGAqLDSR3t7tbe4XOuhuh03QrhJ7eEW028uGiYxMJq2lq4Xcf7KKmqSXoWE7NSiJj2FgmD0/FZjGpx1YaMGs4Ej+03ORYtR5wbJZdd3EWXZVdo3Riz3lqnSp42sJ1KyCLP5Dn0m4sqQ273jFBWw8cyGePwi+KkX78DiU/24kzfhh7jPJN4zbvMMp9sq9km9egij+Qz0OP1xfy5kmL5PFSVNVEUVVT2M5hpesY6J3zUPN+E+p2MPf3r+FsPYoJSAIBIgIoONmwZ4V/rnKrHDm78WO/0bNXgtJ18J//p4+0eSR485pjnwoGf3fjvrXwyW87mlomwCXPgLOqyxpAbc1cW7uHamcbZw+LzLfvaNFO/3h5Qxkr5p/F2/PPZm+VkzW7q5jVMXtX8ngZbDNysN1DZpyZ2BirWhj/5FXjyU2O7fKiaTYZWdlbEZYBiHZknoIyyzkvJRYfPoqrQ4/R+9NH3zM9L+X4febaeuDUsfLfiNLMVbObtrqDXLoqij1VmzAbwOd7iGyDQ1cDWN+ib/5qk7z4fL6g2lKAU9Nj1XnX2vKKMRnx/OIH+erflxpR7ZhW0526267eb2PSWBLqdrDNO4xiKYVLni1k9b0zxXkv6DGiBlBwcuGRYNlUuTYoECUtlHoq3PypXkS1dxjJOr6TR8PV7fM/9+O3YeT5fX/soTjB6n5+98EuXt5Qpls2JiOe7yqb1AjS6NQYSupcqk8awPDkWJb9ZFJEwk/QOygRwO2HGlThnZEQzeVLN7C3uplT02IprmkJ8oJUWH33jIhSon2G8rfhleSO3w58KaP5oesPfF/d/QlBL//0dKYMH8yB+lZa2trZcaiRidmDyE+X512XVDs574nPg7aLNhvZdP+56ghCLb1Vd+tyuZj7+9collJUESvGxgkC6Y5eE9+0ghOL1iPw+Z/ln6EwmeGWz/U2Kunj4Nq3/XV1ju/gr2fr07sWK9y6Xp63e8XL+n0mZPbmO+geSs3jCSD+ABacmxu0bJdG/AF872jRiT+AfTXNmIwGIf46IdDM+GhROr3X3juTlQumkJcWT2VjG3ur5c7V76qaw4o/ALfUdSq0T1H+NgaPUMs6vCmjeWfi37sl/rISLABYzUZ+9uo3XPXcl+QmxzI+O4nrz85hTJZdPS9DlVeAHD286OkvgsRfb452s1qtvPurn5CdFN/r+xYMTEQEUHDi0HoEHtOMYbpvP9gSQ6/rkeSmD5AvEADPTZfFn0LqqbLoCxRXHglemCVPE+hipNQx5QSJBtY0tbBkbQmbSmv57rAzOAKYFkNpnQuXptt0TEY87/dnv7njjDZa1920opKWBLqMsBZVNTHnqXXqY8XHLhR5qbEUOeT9js1MYOXCqZH//nrxXJY8XsprGkmTKpm8rARnO2rDSiBRJoMqagfbTKTabXx/2KnO7FUIjKxp7WBA7hIuOtzEgjf0dYj5aXHsqXIyNiuBJ6+a0CcR7Z74egoGDqILWHBy8tWLwY9n/iL0uiazPEJNy82fypG/uhL5seM7v5l04LY3r+lfYssj4XtxNobKLfgyJmK4qfdnFPcWyfExPDS3QHehcrklPtxeybghieSnxyN5vKzdXcXuQ03MGpPKmKxE9WKmTKPoN3Yjx4HAi/zuyoagySnZSTFdCgHJ42Xe0g3s6Nh2xGAbz143iby0+KBxe+V1LWQkRKt1gDaLiU9/MY1pf/wcKUBNacUfwA7thJiuCDXRp4fnslYY5w6OwdlR1ucDTHiC6v5Wzp9M88GdpBvqaRt6JnOe/QqQ50cr4i0wshZKfIdKfY9MjeX9hVOpaHD1qTjTdhmf6Cj2QwfqWhiaFMOIVNG4dSwZmN+ughOTM2+SRz5pH3cHixVu2+iv9ets1FtXdjPHGKm2BHOHlYWhcov8OHXkcT6qztFeqOJsUVw7+RTdcz8cl8UPx+m30U6j6Bd2I8cBl1ti7uJC9jicjMuy8/z/TODixRvU58dkJpBpt4aMCGo96A7Ut7K/tlkVfwDFta1c+MwXOl877evlp8apZtCt7R5a3PDewim613/xfyYxbUQyVz73pbrvsVkJnacjtRG/cBN9eoC2kaW0tkX19Iuz+FgRJXtObvcOY577t+SlxDJ61TyMh7cB4DNbOS3jDb6tbGXcEDtv3XJWSPGmfQ2tPZHShb7jUAMj0+JYMf/sPhd/JxOSx8tli79gZ2WTuiw+ysBHd09nyKDjWFs6gBhY36yCExtbopz2/epFWfyFS/92hlLr15+iexFQ6kmhxTuM8cYStnmHEeNJIe94H1QfoJ1G0druYVNpHTPzU4/zUR07VBPrjkkm2w81MOVRfdPBNacPpaLBpRMlpTXNeLw+7li+hb1VTjWKF44dHUIm027lwqfXq+nPPQ6nLo3Z1u7hvnf8M3bHZtk5Jz9V7cAOl1p2uSXWF9UAMH14Ita/X+CP+N34cfiJPt1EO3lj3BA7r//8TDaXH+GshDqinysCYJyxhBHmatpqHBibtqnbGiQXb13ooXzQTFW0hYqsBb6GInS1XeiZdis/WraRXRWNjMlM4P3upMOPAf0xbVxe16ITfwBNbh/THlvHl/fPJN1+ckQ5+zMnxtVPIFCwJYZP+0bK0Ub3uph+0ev1TXUt1LV4+bH7t2pKa6VkoKTa2a0v9P54EQhkcm6SLgU5OTfpeB/SUdOdz728roU9Vf5ZsmYDQenXidmJOlEyNsvO3cu36C6mnYk/kGsuM+1W5i4p1NW+jUyN4+lrJtLW7uHON7dy4TNf6Lb70xXjdCP6QqVC5SjuanWiyDBDJWujNRG/hoNy2rcX/kYCRxaaTUb5hsGThCtlPNbqbaptCsB2by7jjB1zt81WzLnTGWaxqg02oX5HoV5D+9ywlDh2HqxnV4c1zK6KRnZXNlAwZFCP31dvcjT1o0f7up2d99lJMRRkxAeJQIA/fVzME1dP6PNjHOgIASgQdId2l39MWyiPQG19U8YEmPec3ITSnYtch4CU7NlcvuwrTWehiVJfBgB3vLGFsrqWiL/Qj9dFoLtYo8xseXD2SVED6Gx188G2Sl7btJ9dlU0Rfe7ZSTFqBA6Cxd+p6XGMykjQiRKP16dr3AB9s0Mo6pwuviiu0YnNnMExRJkMuvF6gQROXwmFHMX1N/js96WyrSN6rUb8erHEImTkzmTGfMsnzF+yktWHbURZomht9/DrlKd550dJmJsqYZjsp1nT1MJFz26gqrEt7O+oq7q77Qcbgx4fjQDs6c1aYMMPwLq91SFT2L2JYo59sL6VwbEWdhxq5J+b9lPkaO70M31v0TT2HG7i1n98zcEjflPuX17Qf8pvTmZEF7BA0B2K1sBrl/sf//htSBrmv6hVbIXnZ+q36U6hu0ZAulLGM+bAL0IOr9fy8k9PZ8bIzk15A/3LhH9Y3+JsdTP2t6uDOlEj+V253BIXPLWOsnq/lcnDl47h9JxBQc0bEDz7eWRqHG/dOpkrln1JcXUznaEIxZFpcfzvD/K56e+bg9ZROoEjnYfrckuM/91qndWPCQ93n2Zh0Y/m9HrZRWdNQ0qHcJb3MIeM6WQnJ6jHL3m87DjYwLy/btBt05O/DWerm4LfrlYf73xoDnG2qB69H23jzimDbCy57jRV9Ee6HcCIlBiiTEa+O+wX+VEmA9/+v9k9PrZwr3vZkkJ2djJBpavPVPJ4+XZ/DW98dYj7LswT6d+joDt6TQhAgaA7BEYAB+fLdjGpo+En78ETowhpQLFgkzyHWJk+Ei6FXFMMiyepD2+1P8d/qkJ3HO7VdGHmpcTy4e3TwkbMdBHA3hhML+iUv28o5dcffBfyucDpEaHYebBe13jx0R3TGJ0pj70LFR0KFfn54TPr2auJ8HWGxQjtXtQmikAblbzUWD5cFP78CsTZ6ub8pz6jotE/beObX51LcvzR+dYFvvcum4bCdBw7W91c/OwXlNXp/QLTEqIpvO+8Hv1tOFvdrNpxmIvGph+VwAq04oHIPv9wJtWBDLFb+fiu6b0mArt63dzBMXx05/QTOpp/IiGMoAWCvkIZ03bdCvlicnirvNzxPTx5KiHFn8UGK26Rhd0Ls2QB+drlHUIyYNawMuIKIHMit/9odtDuThlk48mrxuuWFVU3c+mSwrCGvFrTXyH++haXW+Lhf4UWfyAbY9/4t2+Y+PAnuNxSyHXMJlPAY7/Qu3zpBs574nPmLSlUTZiVejwlQlhe1xKx+ANZ/IFsaBxlMgSdxUWOZioaIp+LHWeLYt19s1kx/2xuPPuUbok/JZ24u7JRZzJ9pNnFzMfXqu9d8nhDNg3pCNFx7HJLTHpkTZD4A1h1e8//NuJsUVx9ZnavRtcUihzNzO3k7xvk8oGRaV1Hzg42uJj0yJqw5153kDxeWtraGZoYejSlxSh3Z1+xbOPxMQsXdIqQ5AJBd7FYIW+WHF1IHS2LPwBfmML79lbosJ5QBaOyvKxQ3heaVNb/fIzVKc8qPrynRrcrswH217dy/4qdQV5se6ucndb3nEz+Yf2ZDftqaY/gWheuy1nyePnF2/5u1REpMQwdZAP0liQ7KhqZ89S6kDVW2iaR7hKqdjAvNbbbUyfMJiOn5SRxWk7kjTyB6WyQI6a3nTOCRRrT5R0VjZTWNHfdNKSdGdxRf7hhb23QJJq0+GhW3THlqCOUvUFucixj0uPYdVgv4Pd08vetREZXzD+b8574HIfTrXvebACvD5R33SZ5j6rDXok437V8C7tCNHEoKH8HOysaKXY4GZUhMoj9CSEABXoCJ2icIDYpxwWTGW7+zO8rGI6MCfLPyq3yZJHaPf4Ucs5UILT/nRkjf1m9V7crpSlgR0UjH985ndvf+NY/jSFLjIbqD2TYQ0dDcgbH8Mw1E7n6+Y20tnvDdjmX1jTr6qmKq1u44q8beOqaiQwdZAsSdqEK+5WIb1FVE/OWbsDVEdlLjInC0dRGKJR6QL+gMtLa7mVkWhwfHCNbk/K6Fp34Azliuihg4oZCl01DJrOu41jCyBP/3aM+bTHCygVTGJVh7/T9HcsOerPJyPu3T6fY4WR/bTN/+s8eiqubKcgM7bUY2OC17PpJXL5so36dAE0fbTb2uMPe5Za4dElhtyLMAAfrW4QA7GeIq7tAxiNB1S5466dwpMMmYdAwmL8eokXUKCyKr2DRalh+TcCTBrjpE78AVGwvQtQAhkplDU2KCWmRAGCzmMgZHMNHd86IeMyX4OiJRAjkpcVTkJnAzopGtaYOoKy2hTirmS0PzgkSLNpGhlDsrGxSo31v3XoWB+pbueetbew41MDYrAQ8Xp+aClYwm4yMzrSz9df+1/u+sknX9BBtMtDm8RFtNrLp/nOpa5HItFupaHCpP4+lbZAyazdQBAYyOi1WrXW0Rpk7j2RpOo5Lq5p0EauVC6ZSMCSx09c6Hh30ZpORURkJ5AyO4a43twJQ7JDnLucmx3Kgo0Fo6CAbm0rrdF2+8TYLYzLiw0bmcgfH8uGiKT2qyZM8Xi5Z/IUu8xApWYm2bm8j6FuEABTI4u/FWXKESkt9CTyeKxu3RsWKiGA4vBKs+Y3/cUwGzLgLJl6vF8+K7YXJrKZ9FUKlsswmY9iLYWu7h4oGF8NS4kJ6sQVyIngAHhe68nTUEKkQMJuMvLdwKuV1LSTFmJn86Ke0SV45oub2UFrTTFqClbLaFkxGAxkJ0Zzxh7Xq737j/52D1WzAFRi2Qb7AVzS4yEuLV42Y735zK3OeWhe2S1crkOKs+r/fto50b5vkpa5FUqOIgT+7orfOL7PJyMqFU1Vj67KaZua/9m3Qes/8eFKPXiewDi0SW5twk0COBRtLalVLHZfk48JnvtCZfFvNRlySV102boid3ORY3l80jWKHk8uWfKGeR2MyE/jL1T2fT3yk2cVTnxR1Kf6GDbZx8IhLV0pgtRhVwS7oP4iruUCOTAWKPwWPG148T/5/xgS4aY0QgVo8kpwCrt7tX3bDe8FziLsgVCpL8njVYvzRabH8YEwGq7+vkj3lhkSe7g0cLdZfPQCPOV15OgZQWtMcsRBQ6i1Lqp1qBLC13ctFz34RtG5Okk0X/f3PLkdI8Qf6NL/yO1TSxTsONVBU1aR2C4dCO76sIDMBg8HAjoAJF4F0NZu5tyNkWoPpEalxajRVSVErIqcnGAyGTh+HItwkkL5G8nh54r97g5ZrTb5d6rnl4dUbz2DqiGT1s48yG3Xn0V+unhDRzWIojjS7mPDwmojWLaltpSAzgStPH8JDHZ3wrnYvB+pbe/z6gr5BXMn7G704RSJiBuXI4k4RgYYouT/coy8kpnLrUc3tPCmpL9PX/6WOlj/PmuJu/w4DU1m7KxvVi/v3Vc088+NMFs3KCxtp0UZhAHVEVeBosWMZwejXlBXK4g+CGnICkTxe7u5IxQGMTIsjM0ytn5ZImjHK6lrVVLHNYuKisem8tqk85DZPXjW+U3F1x/ItfHTnjLDraMeXac+TcJE7bW1qtNnI5l/NCupyDYyQfbrbQU5ybK+UJGijqb2Rkg6M+EUSAexsEkhvo7XzAXS1oIHWPFrGDbHrxB8EC9ejicD988sD3Vp/Z0Uj94p65H6PEID9iTCeVX2OySxH9qr3QMMh2SHfK8Gy6XIaWCFjwlHN7Twp0XYZpp4KP18NL50vd/2mj4eb1/bod+hyS/wooJAbwnfyaqMwY7Ps+Hw+dlY0kjM4hjLNqK/8tDjRKKKQM1WO/AU05IRKZwY2ZuytcnLVc192Ge1SxENpTXPQuDaFsVkJvHHTZDaXH1GjbCsWTKHY4WTe0kI1BViQGR90Ec9NjiUvJZaiDsPnIkczpTXNISMt2velPYc6uxnQ1qa2SV4uebaQ1ffODBIa2lKFm/8hm0kPT4lhyY8nkTM4RhZuiVGYG8q7fWOkPee7MhPuSqTlJseqxzo2KyFiUXQsOugDO6ALMhPUYx2SaOPgEb9tTZY9mkMNbRRkxvPU1RNDiu3eFK7XnzWUPwc0pI1IieGJKyfwi7e3UVTdTF5KDNEWMzsrGhk3xM7Zwwb36LMWHDuEEXR/IsAEmPmFYLYem2hgqMij0hHs9YDRJGoAw6F8dvYhUPwpvKlpBplfCOkF3d7l2u+r+Nmr36iPMxKi+d3cAqbnJYdMw3VmxqpEl/LT4nl/Yc+Kv09aAmoAw6Uzdx48wsWLC4M2787UCCW6I3m8aurRZDR0GilzuSU2ltSSYbcxIjUOs8moCh0lIlbb5OLK5zep2zx3/WnMGp2m26f2feWnxvH+oqkRnQehpnqEes+hzIsVrGYj7VI7H8X+jpGeoj65ue1OGrq/1sOG+hv+6I5p3Ll8K3sdTjUCaLOY+PqB83A43cf0PRxpdvH3jeVMz0shzmpWz9tQmQfluPrrZ30y0x29Jq4E/YnAVOxLs+XoROpo2W4kcOZsb6WKPRK8OBsqt0DKaLlztemwvG9tLVu7C4rXgj0LUvKFGFQwmWXx98I5fk9AhSMHui0AJY+Xxz/W76eysY2b/7EZq9nI1l/P0V28JY8Xt9Jk0O7VdZ6CHLkJrA8SdKB4OnYQruA/lHVKd2x3jjS7+PuGcmbmJzMmK1F3cQwksObu3FFp6nNaoaNtBtBy6z+/pSAjnvcWTVN/39r3tcfhZO6SQlbdMb3L88EaZWbzr2ZxybOFlNa1hK2By02OZWRanGoNYsJDtsFBuS8VlwS5Bocs/kA1ZO7NUpLuNGr0Vz/MwEjq2Cw7BoOBvR3lGz7g0XkFzJuYhTXK3CeG052RGGvllhnD2FhSiy3Kb1SufJ7a81Y5r/rrZy2QEVfw/oTJDPOeg6WT5cdKfZLje1hyFiz8Ur5g9XaquHq3LP4Aqr+HPw0HyeXfN8gWMS/MAl/HaKeMCXDDKijfFFEHZb/FI8nvv7ECcmfI49q0jyN5X0ojSKD4A/jsD5DXvfmnpTXN7K4K3WnnCjBwDWWc2yZ5yR5ko7zDKmJsVsKAE389jTyEK/ifMnyw2oSg8KcrxkW0b20B/ZNrihidHseds0byxH93U1TdootYHWl2ccYf1tLe4ccXONpMK3RCiT+FnZVNai1eRkI0RVVNZCVaOXREnubRmalwIHG2KFbfO7PTz9NsMvLBwqn88Ol17K91siLqIcYbS9jmHcY1nt9R7kllrymPkZ4ifBkTMfRyKcnxatToTZQO6GKHk/LaFoxGaHXrf8fjhiQetwi+XA+6WlOSkMDymyfz9f56BsdGc/XzX4YfySfol4jfUH9j8AjImOgXZApHSuH5GTB/Q/B4o9pi/5zZ7gpBjwTv3KhfJrn0+145P/h4Krf6hWIEHZT9Eo8EL5znn9JhtkJMJjR21D1G+r4CG0G0HN4u11amj+mVQ442GXReccUOZ0ibmPL6VkamxvLU1ROJ+v/sXXl4U2X6PUlu06RNm67pSqHQlqUti6ACZVEWN1QEd53RcVxAVtFxHHXUcRydcVwYVlH56ei4grIpbizKUhYVKZStC7QU2tJ0b9M2TW+S3x9fvpvv3tykSRdoIed5eEr2m5u7nPu+7zmHu7iIX3vkjiXFmfGhWO+DibG7uSmNmsPnj4wReehZbe1OzwAA/pt9WnT7+DmTyNqEVqzi9Rpc4SB/gHxSCFslkhJSKegsnpx4gJKk9hS+FGwlx93616g5/PD4Vcg/ehBD1pF9aJjyFH6dlYIyLhFPfBaKxnOFCG1NxZdQdunJh1MpsWbWaJcKVG/En9bkCLOiASqxSKW4qknYn92NDnRX25XMgzo7C0fKGjDy5W0uqSruEm786HnwE8ALCbk2ro13Vv6kqMwDTm4HQhOcreK44U6C5ms1sM0MHPoMqJJYDSjVgM3izKSVkj8KShTbUVD2WNQWO8kfQL5PAyN68fZ7sUIQTuNcLxT1pT4RwLjQQJeTNm3r0qxXgBzoF7hJSACAfGMTHl+Tg7wKk2AgXFzdjPL6FozpH3n+rtBbTcCR9UDGjE6birMt0H7hWiy/dwQGxRHbE6qetNrsAimmkWG+2E+4a1vVtYhV8QfP1GJgbEi7J9m02PY/OyKIw/6iGhGhC1ACbVYbzBZe/Fs55gdTY0LQxluR344vm5T8JYZpsOaR0eCtNpf0mfa2CbOFJ6ryCnlLIU6lxJD0YbDvHQFF+UHY40YgODYVqGrB4fImAHFAmcnn38QT6Gzl45/nILesoVdbHZXUNIuEQm0Sgj/v04PC7yl3ccNbbZixco/DIFzeF7KjIF6lSoEE9g3X4nSt67nKXcKNHz0PfgJ4oSDXxuXNwMrRQL0byb1KC3xyO/l/3HBgjmPwm7aMfZmtYT3QpNBGAvdvINVIQL4iKVouNZA40v3jPRFtZtKy1fcD6ovln6MMEJShHsHGTekTSdV09WTye3IaoqpmyT7gcX7zl9O1opP241NS8eZWMj91pKxBaN2V1DQL80EAkGrQYdndI/DkF4eRW1qPNEMw8iqc9i9TF/+EM7Vklk2tUuC3v07p/jmiVhPwz0QAduCr+cDTZztFAtkWaHFtC25cvgfpcSFQKBSCSndAZPckDozpHyk6Af51w1F8/vMZ2QojW1mbkBolJG4EKIn3nLRyd8vKvfh6XpYw16dWKaBQAA99eAABKgV+eWYSwoI1JCrN8f2PljXg63lZeHzNIWE7oAbSrJG09GLibJ0ZZfVmnK5uckmf8VS14a023Lx8t0A43c7aqTgoHiL7g8LNNi5XPfW2GildppuW7sRxZmSiN1sdRQR5/t7sWstljgUURVVNwvaRW1rvNdFm7WfcVRaJV+lUQZSUGKYRDMwBYEhsMJ68bnCnLi6liThSAZQfXQs/AbxQkLZx874D1v7BOWNHEZUGjHoY0IYB6x923l+eQypL/bJcws69AuuBJkVTOWkp0wP3H78D3rmKzAeG9wfUOqDiMKAIIMtrtQBvDur5beA2M5D3PVD2G7BnGQDHfI1STaqqNAIPAKAAnjjR/vdhVaSUeMdmAE+ddt6v5JxkP24EADv5/dxUbOP0YgIzMS0KW48bXeab2LmngTE6bJxLlJ00IeKxz8SknZI/ALBY7Zi2dBe2/enq7j2w5n4B52nLTm6P+oPbp7dHApIigpAcEYQiRjwhjbw6We3crjPifbOfMFt47CqoAgAXxbVGzeGL2WNFZs5yJ2FTi0VojQVySgyICkKr1Y5+EVp88ehoHDhdjxc2HsG5RmdFsbi6GQdK6vDLM5NwoKQObVYbHvqQtHDbrHZc/sp2HPzrFBehwONrD4kuAhLDg1BY2YTEcC2W3HUZ1JwScaGByD5ZjX9+ewLF1UTIEa/XYMEnzja0N1WboqomUbUxzZOlEBO/BpDIMkqCAWD+pwfxFaNElq4zOb9BORw+UycifwAu2Awgb7Wh0GhCeX0LMhNCsO14FaZlxvp0kfX9UaPXz00zdI2lE1s1BOQrixRSUdLB56Z4TdKkrWkp6SQVaTJjqOEUgEIBs+NiKz0uBBsZUZMfXQM/AbxQCO8HxA4HzuWQ22vulX/ezNUAFwh8KsmZVamBj2cSEvHAd0D9Wd9mAFkPNJUGsDJtS5UGCIl1mhnXnyXkDyC+gNGDgbvXAp/e7nxNT28Dt5qAf/VzJdgAaXePnQ8kjQb4FuDcESDzNs+VqjYzkL8VWP9g+3OQLNlnK6luKrb9IoNEFZw/fXEY6x8d62L74G5ejVMpoVIq3GaBUpyuNXdpK04W8cM932bAmg67a0lyKiW+mp+Fy/6xVaiipceFwGK1o4AhQhSL7xzu1UmDEr85Hx1Am4OvagOUOPicWHEtnadMMwSLTsK8laR90LmoVt6GY+fIchXXtGDMP39Em428d/8IDU7VmB2fpcL97/8itC95qw0BKoXQAmyz2gUPvjfvHC5YruRXmDDQoEOe0YQ0g04gg4WVzVi0JgffOJS+12bEYfLgGGFbkbYa189p3x5IGqP25u2eTalZlNWbBfIHAAVGE6Yvz8bmhePBW2249j+7ROvsxmXZ2CrxG5TDV4fKRLdnDo/Hv31Yrs6CXrCMTArDXe/sc/F5/Mu6XOS+MNVrEnhtugFPrXPe5gCsum8kRvUNw8Ez9Xj9+zwcLW9EmiEYm+a5krSO+ByyVWVA/qJGCpbMsYTQ0/OnL9uFo+dMiAhU4IXpQ/HOrlPCMSo9LgRTB8eIYu/YeufR8sbuP1ZdgvATwAsJq5sKHIv3rnGdKQOcKR1lB4lqtamatB+9JYABGkJYaJWq4ghpWwKEDFITaEowDYOdKtfK48QKJmogUJXneD+td+3S8w3qZfjp3fLkj+Kbx50VucRRnt+zzQy82lf8u7AEWBox9qdCZ5VWWgGUqdiW1ZtFEU4FxibMfGsPltw1AkVVTaIWjbt5tXi9RkQI3KHVg5LUE7weNI/JAGKGARWHyN8Y95Y4rOmwp5akTqvG4ReuwZ6T1YjTa5AaEwKT2eISVeVt+gFLPFm0tNlcloE9waYZdC4n4aKqJpyucb9f0xn6ljYbnrs5A4nhQThb24wH/ks8Hw+X1mNXQSXi9Fp8/vCVuPPd/QIJLKppRklNM5KjgkWK1zWPjEZZvRktFquoOpkvUfqy24pUNZtiaL9dKv2dAwNUbp7pCoPOlQDlGU348YQRr/2Qh9J68TGu2PFd22vjzp3UH+/vdYpsnpk28LySPzYlRSqGAAiF2Zx7DndekeTVe9Y086LbXy0YJ0T7XT1Ig/Gp0S77nXRfXO9ITmlv3zS1WLDuYClsNmBIrE64UMlMCPVYWexI9F/euUYcdbx/TasdC9ccEj1+tLyx3QtW6QWIH52HnwBeKNQWE1EHi9BEoOGs+D458ifFO1cBsPuuxqUeaFYe2PyEZPkcYoiyg6QC+PBPDquTY4TEbJxDyF/UIGDKC8CAST2v/cvOWXoDb2coi3a6/i6cxkmApRFjJfudM4JezABK23wAEXRcv5Sc3NtTtvJWG25btRf5RhP6RgThtIzXHEWlqdVn1aBPJwAVBzyy3SvPSjJkrhIqgJ5akho1h0mDnZWHX0/XiR5/6eZ03H1lklffhyWeLLQBSpdloCdY2rrqDNnITAhBWJDG8X+Sz6sNUApkEAAGxQTDbLGhuLZFaG3KVX77R+twvEwcHZcSHeRyImd/a3cpEbQ1Z7XZRUbVHU3RAIB9RTWy91Olsuu68a6NGxUShF+fvRorfyzCnKuTERVy/lq/0pSURL0aZ+vFQiEFgGmZsV6/Z1JEkLAtZCboRRUvOZ89d/uiO+JMf/+IIA4jXtommincOGcsggI5j8bkvNWGn/KMXnsuUvxWUuv1OnCHJz4/iE0L3Mcc+uE7/ATwQkGfCHBa0nIESEv3jg+AjfOc7Vav4diNO9qGrS50mk9LYRhCTty8Gbh8FhA/FDBVOdu/VSfInKKSE+ffXohMYynY1iuLoGjg2leAPctJZYoqd72dodRJqlKhicDc/U4CLBcxJpmJov8/W9uIv286gedvHoTEcHKwZ/3AFnx2UDDXpWivRVNQ0SgIIk7XNMMQpISx2fXqOZBT4PK+4T5fzftiugvA9bu7ARkyn+KzEAAArBJRhSEk0OsThZh4KvH6bcOg5pQuM4BsAgdVnErJeHJUsHACbw+3rdqPkEBOeJ/3/zBKRP4A4ERFEzbOGYMQrdqlzc+uc95qw5NfHBa99mydGbzVJpq3EpY7QY837xiGPuFal7msW1Zki2LvWDWpt9UldrnyzjViDjNvqFYCFpliTqpBh0VTUhGgIuve298vKiQIz9/cNTZLvuDK5AhoApTCnNrZegvWPzoWdS2WDs8AAoDdZhP9BdyPR3izL9I0mTCtGo+tycHp6mbEhAS6qMN35FdiztUpHsnf9BXZOMpsGxnxnquFFJclhXvz1T3iWEVTrxX39FT4CeCFQm2xk/wBRI26ejLJj40e3AESCHEVqiugUgMPfEvIEVVyCo8FAtZWokbWJzorbYbBpNr132lOm5qHtl0YEsjas7DrtLkSSLjMWZnSJ3o/Q9lmJrY7FJEDgdk7xdVPaXvdTWX0bG0jxr1KZrl+OG7E7qcmiEjgoLhQfLNgPHbmV4pi4dob/i6XtNPa7EoA4jNuQpgWm+ePRXlDq09kjrfaYLXZhUpQVw/ca9Rch/zD+kqqUdLbcqhqbBYqR5R4jkwKk43YYistKdFBKKwkVVWpzQynUgoiHJr9q+GUMPM2DIkLwTGmzcVmNOeWNeBcvRkpUUEorBJXbHPO1OP+rGSP36WkpllE2gDA7GhhZ6VECcsufF5pPaYu3ikQX0r+C40ml/fJZbYLX5Id5EzKAeDZaUPwwldO30zqZ1hW04DXPv0GJXYDMuL1+PKuOHCR/Xts4pBGzeHZ6wfjuU1HhfvyKxqFdq+3bV8WhUaTMEd4hJl7czce4c4Am1YLhyWGYsy/fnKpcFfIJNu8ubUAW48b3V4EFlU1icgfADxxTZpXRH1gbAjSY3VkBlCrwnM3ZuDp9bkwy7TN3SEpLLBXGnz3ZPTMPetSRI3Di+/cIWLvYrMC70wEbB7m1gDnHF7EAGDWzo61YSNTnL6CUYNIVQ8gc4bvXwdcMRsubmJWxwGk8gSpINJKm/E48PYEoOYkuV2eQx5nI+XOF6T2LO9fJ1ZLs5Upb6xzrDyJe2PJ+Z0fyq9zScSYHP6+6YTL7Xfuv1x0H6dSYkJaNDN3Jj/8zWLsgEiBdABAbYtzrmhAVBBUSiXyjSbcvmofrLzzMQ2nRLze/fbDW224ZfluHClvREp0EL5bOL7H2DOkGHTIiAvBkfJGZMSHtDvTVtXYjFEv/wgAeG9PMX599moRUaK+iWX1ZkE0QQkUJX9SsO3V1BgSxcZm9lp4G65bskt4vnRu7On1R6DhFIgJUaOCUQhH6QLb/f5s65CCttHZZZeCEgM6e/jKZtcLz8GxOlh4GwoqGj22B6UoqWl2IX+BnBLTh8fh4/0lyDea0C8yCMXVzVDBik+UL2AYdwqHbckIqLKAW1naLbnBXYkZI+Lx/KajsMP3dq8Unnw93Y1HyI0DmC08hv99C8y8jXhJeuBY0cEBqGxynmN8sdBRqxQY0z/Sq+/GqZTYOH+8aDmvz4zF3lPV+Nfmo8ir9DwPrwLwzUJ/+7er0TP3qksBkSnylb74EeQxFUcqZ+9M8Pw+U14EolI712qln0WJEp31AwihMwx2/1reTGLTWJEIJX89ASzJY+fwOrKuaovFcW+GIU6vxA7g+ZsH4YfjRtFtOfjaetOoOeQ8PxWrdxXh9S1Ok+/EMA2W33OZMEtYUCm2zzDzNqz7rRQzL0sQWp8sqWGrE4WVzZj3yQF899hE37+4j/AUNk/BqZQC4fJmHf1na6Ho9uItBXhw/ABRNXT68mzkGZ0m2rTSwqJfZJBgYTF9+W4cLW8UWVbQE6ncjJ6caMDM2/GXiSn429fOCln/6ParmWzl0cLbYGw0C35s8XoNAjkFWnlp0w+Cr2GASuHSfqY412AWiKsv5sLSWdbkyCCsnzMGv3/vV+QbTUiL0WHd7DG4Z/XPaCw7gWFKMnc8VMnYMXVDbnBXQqdVI/eFqdice65D7V4WUl/PtBidMGfpaTxCWpXdXVglXPy12Qh5kpN6ZSbosXbWaJypbcHjaw4ht50YPXYGtF9EEL6en+XTmIZ0OTVqDn0jg0Xkj40rpHjllgy3xyQ/Iewc/ATwQkHFAY/8RCpKxuNEWDFjlZNQGE8ASpWzMsfOC7L46Z/Agz/43sqUWx56kH3gW+D1VFIB5DRAwzkPL1QAfce6ikTsdmJxEzeiUySpS+HlLJpbsC1lw2Dg4R87VZlIDA/B7qcmuMwAysHXUHWNmsND45Ox4qeTgkrxu4XjoVFzgm2IHJ7ZcAQvfn0MB56dDI2aE1XEFkwWr7vCyuZut2ZgW6+ZCXrAbhfSHtY4Tl6sWMHbdZQcKT7JbT1egeduTGd8FUOQV0HI7uHSepypbcEbdwxDcyuPW1buESo+G+aMAadS4sjZOkHFeLS8ESfKG5CRGCb6DGmqQ0p0MAolJBwAhiaGCubNChDTZDrL5wmcSin8FunQC/cXVzfLkj8AiNcH4mRVi8uysahllKm5PlSIpIKZ5KhgkVFxfoUJ5Q2tpIJVlQHbho+glBrOGwZ77216gaDTqjvU7pVCztdTGrXX3ngEb7XhXIOYQEXpOFSYnL/hW/dchhQHuaTbzHo3giAWHZkBbQ/SFvaaRxyElKa6JOpxx+V92hW9+NEx+AnghYKVJ2Ttwa1EJZp0JdB4jkTB/d+1Tn9AQybwyE5iY9JY6vo+5w45TZqp8KAzbRMrD7x3ndNmhjcDW5/38AI7We6oFODBLc7IL05z4UUgXQ22pdxF3ys2NBh/uWEwYkO7frbFXdXgy0fHYNrSXThdK68wb+VtuGl5Nt65b5SoIpYYHuRSSTpVafKpLegr2PYl2948XFqPm5bvRgFjTNw3Igib52d5VYW58/Ik/OObPGGwoaKxDWX1ZqxzVNFa23hHwkYTMhNChQrJQIOOtbVGTTOPsGDg8Flxde/w2XoRAeStNiyQGHPfP6afaH4MIEP1dS286DNuXJ7dqZPd6Wr5qLg0Q3C7MXJSeKvOpWBJKQA0t7qOtHAqJfrHhAEPbXVkj88iF72GIZ2+yOpNcOfr6S1YcsSmv1SYeOG3zkwIxdT0GJf39vYC09cL0faM3eW+c2pMiFui6bMAzQ+PuDT2rJ4G1p6ECwT4VqcSNby/04IFAIy5wJr75ckfBW0jU+uRjrRNaKJFcJRrW7qumAhCrIzFAb1N5+la6pxVw++eJCKIHtq26RQ6W0VkcD6uZqVVA95qw93v7hfIHx3AB8SRYUUOgYLUK+7L2WNx4/Js4f1mffQb0mJ02DTXt3aQt2ArBGwFUI68nK5pxsiXt2Hj3Kx2ZxN1WjV+efZqTFuSjQqTRdT6WvR5jiCE6BumwT+mp2P6yr0AiHfdwBgdycJlXnNDZgye2XBEeP8bMsXmuCU1zSLRRyCnwIwR8Vh74Kzju4XizTuGIzkqGGYL7xLf1pmTndKRHcxi09wspMXocOOy3bJVSDlsnDMG6QlhHd5GTS0WYT0CwJC4ELGVjIojs8J0HOViunj0Er4SLBYsObKDjHycrTOLvCLPZ9vUG2N3gHxnOmdLl8/denAnevGjY7i09q6eAtaehHeIKaivHEv+KEQRZRLEDCWt4vKD4gqgL20TT7nAABA9iIg9WFgt5Ar9ge9I1ZKSP4C8T9FOIO0a75fhEsSFuJqVDuazmbR2AJwS4G1OE2Xp1fmguFCkxehE1jT5Fc5Uh648udBZH1aMQb9Da5tVmGVk0eoQWrRHqHmrDff/3y+oMFmQFBaITx68ApxKKbLQAYDTdWbctspJWtJidPhy9hgXpbDUwJdWBinYE1dyZBC+mkcqlXIVH6PJ4mLR0ZmT3fjUKBfBiVatgkbN4ev543Djsl1uhS0sAgO4Tv2+mw6Vi27ffbkbn8YuvMjq6ejKeTZ2G8uID8VjU1KRGB4kXAz1j9bBbOHxw9FzOFZaj/TEMFw9MBoaNedVFrCv8NbY3ZcL4c5WSf0Qw08ALwTC+4lFE+2BVeZKceu7ZMauMzOA7nKB7/oMiEgm7/fedeIYM4DM+xVnA3aruDoIAKHx3n/+JYoLcTUbr9eIqn5S8Dbg/T+MwvjUaOHgypJSTqXEprlZuHn5blEFLs9o6lIC6+mk0D9aB95qQ3pciNv0gPYINZtMUFLXiukrsvH9InlBC6uizK8w4a539uGLR8UnofZ+S0+xfdJlFFc9nZXBjp7sNGoOB56djBuXZwtVyMc/z8F6R9X2u8cm4tP9JS7taCl+/+5evHBzOlJiQjuk/h6aqBfdHpEU5tPrLzaw2zib5d1R0G2soKIRt6zIxkMfHoCGUyLn+amCOnjo374XeTBqOCV+fXYy7n53v3BhmBEfig0ejOa9hbfG7r5eCHemSuqHGH4CeCGg4sSiCWWAe7uX2GFOkYe5Hnj/BsDuqDbEDXcqhlk7EysvNmVuD6xxMW0+xY8AUqc6X//H70hVT2cAvnrMWXH8eCZZRlakEjOUVA398IjzdTXLVhnK6s1uyR/FK98cx9pZehw62yA7u6NRc/hm4QRiVP3pQeQbTV1OYKUnhezCKmSlRAmPJUUE4T93jRAycQGS/vH5r2dwxAt/wsNn60S3T1Y1CzF7qVFBKKhyXxE7Ut6I65bsQnF1s4ictvdb+jJn1dXbhU6rxrv3jRLWF2smzqmUMIS0PzdZ2cxj3mckwisjjtjc+LJsg+JCBdKeHheCQXGhHfsyFwF4qw3ZhVXCNp5XYcJ1i3fi6wXjcLbOjJLqZiiVwLiUKJ+VtuVM7rKZt2FHfiUCVEqU1be4GHCbeRs2554TdQWOSLwtAdK+X3+wDDa7HbdeluDVnK23xu7eXAibWizYdKgcA2ODUd9iJVZX3TBycqlBYbfbPZ8NADQ0NECv16O+vh6hoZfuTtvloGkZ2jDgtQHO+1UaQvq4QCfBazM7CWN4f+D2/wIx6a4Ez8oTQ2lfTZjpDCAVo7DkkZ1ZpNnAxdmE/FE8sgMwGUnlL3rQJTe70xPBW20OkvYb8o1NgnL2jrf34XBpPdIMOtjtdhc7GMA5D+hpdod+RlcTWOdyE3JJPQ0z4ok6llUB0+8yNFGPdY+OBW+1eZUkYmqxIPPFLaJWK618mi08rvnPLpTVy4tkpNj+xMTzUpHo7LoWVVUd60tI/3D4O/qCjnzvS9nCg02Suf3tfcgtrXepxkt9+zScAjnPX+MT2TleVi8aj+AUgBsRuFABnLlyD/KZ48CWRRMEAii3rxx5YWqnLG+k8LRdmFosyHhxi8ty5zw/1U8CZeALX/OvvQsJWrmrEnuS4eFtQGyG87aVJ6bQdA6v9hSgDnYSQzZxgo1188WEmTUuDpTM37AzizQbOOlKiGQDGxeQZA0/8esRqGsy44Ylu1DW4GzNHy6tFyldASAuNBA3LtuN4hrxCAA92Hua3QG6vh0jTY/oG6HFaceysbN5h0vrsb+oBp88dAUOlNQJ7SWBELYzS6TTqnHwucm4YelulNW3IpAjPnjp8aFQ2O1ek7+BMSHOikU3xh92hWDIXWWxpKZZRP7kvNikyIgP6VDF91Js3wkxfA4leZpBJ/j9SavxUtNmM2/3uP/JITkqGBpOAbOD9cmRv+jgALxwcwamDDZAo+awaf44YaxDmvP81eFyl5nUzbnnusT6hsLddmG28PjHN8dc7+dtPq8XP1zhP1v3BLD+cvEjXNun1YViEUa0wxuLFW8EaInytruXL3owEawU7YRIp1hxqEcbtl4q4K02HCtrwM0rsl0eGxjjjJB7Ys0hgUx8PX8cZr61B/nGJpcKhKfZne6AVKRyWkJMqfpXG6DE/e//IhgZDzTo8J+7hovaxu15FOo0akQEB6KsvlWwtpFGXUkRruXQbLGi1WpHIKfEl7NHEyIlrZJ3cXpFVwiG3FVZkiKCRN6QpXVm4Tad4UqPC8Edl/dBtC4Q/aN1PSYBpqeDJe4U+W48OOWg4RRe7X/sb3umtkUgf+5Q2dSGIfGhQgWNjnVItw/easNH+067vL4ziSftgX6XIDUw9p87IBdkouGU5/W4dLHCTwB7Anz1l7t1NXnOqR1O8UZbC6kE9p9IzJfLD3adCbOKI23ftycQi5hVWUB4mvg5kQO7zrC1GyspFzPcZa8CJLGCGsueqjSJyER5Qyu+WTgBR0vrMX3lHuE1z08bjCuSI87riT4pIghp0cGidpRaCVhsZDj9i9ljsPdUtZBa0eJgq3lGExZ+dhAZ8aFCpXDRZwex+K4RbgUUUsWvN2Bj9Vp5G4wmC2mFSavkXXwx1FnBkKcKIqdSYuO8LExfkS3Y21DbEBpjdym2bLsCcjF8nlqyFAsmDcDQxDCvZgClZulWm5gyDYkLQZO5TeT7qQ1QucQ+ylXhSmqaRUKrBVcNwCMT+3dp+5eFHGGW4s5RcXjx5qH+9m8XwL9H9xTQdrAc4YlMIWQOIBU4SuqoeAMgf/tlOWLdtgLzDpC/XUWgaotJ5rBwO1/8+O3vdc1n0UrK8pHA6ilAWQ6Q/wOpdvrhEXLZqwBJnKApIIAzoovi8TWHwFttmPPJb6LX/X3zcdy4PBszVmSDt3of2t4ZcColNs0fh77hzpMTHVxvbLbA1GrB2Vp5u6J8YxMen+q8MDlS3oipi3dixso9LsvPW21Y6CZ3leJvNw2BNsD9IXJgjM55EqVVcsB3GyYvQNu325+YKMzueQNTiwWf/1yCE+X1LhVEFho1h80Lxgvvr1Fz6B+tE/76yV/HQIk7AKTH6RCvD2yX/GkDVJhzVQqmDIn1iuRIzdKPSWY5/3TNQHy/aCI+eMCZM97SZvVqzIFd/qGJeiyYmtZt5A+QJ8xS3DdmgJ/8dRH8a7G34KYlwBcPkArc+9eRimGABvhToTN9I0BDCFR1Yfvv15WIHtR1ql+2klJ+kMw+As4Wd4DG3SsveUizVxP0gVh572Wy5r0LJqfi4f8dAEBOGntPVbud+2IVo+cDGjWHVb8f5eLzd7rOjFEv/+jy/H4RWhTXtGBooh5jB0SKqoAA+X7SdnBJTbOoyggASeEaGE0WmNts0AaoMLp/pFBhZJESHQSlUom8ChPueHufo5rW9SkxUvg6Pycd3k+PD8VRDwrpS3E+r7tBiXvu2Xo8+OEvqGmSd3v46w2DMGZAlCjD2VskRQQhM0EvSsqhCOSU+OMHvyIzQY/Xbhsq7BveJLrQ2cV/3zZUiFrs7gsBttIth/Q4HQbGdl/05KUGPwHs6bDypBLGevDRFlN4P+CDG8ntA/9H2rTvXesUgcSN6LoqYHg/p1WMSgNEDyQxdF0d18TOG7KgLW4qVPEGl1grWS57VXrA5q02zFi5R3SyyEzQwxDinlhnJoSed8f91JgQtyc1FulxIXj99mHgVEr0CdeirN6M+ZNSMOsjcTXTahOXXZIigpAaHYQCxgD5rd+NwoDoYEFFzKmUAqGms4ZpMTo8MTVNeH/RPF4PMzDenHtONLx/7xVJGD0g0t/OPc+oazZjxlt73D4eoAR+N7ovNGpOlOHsLTiVEm/eMUxkiQQASWEalDgu6nJL63Hdkl0IVJFUGLvNc0XfbOFFUYvpcSHYOG9ct6u4WaFSRBCHr3PPIVoXiKwBkS7m6350Hhf/WbG3o7bY1YDZMISQGuncUcEWJ/kDyOvYWSS2Ohjppt3sDvVnnfOGVjMw8x1AyXU9uaKVlJPbgU9uZx5QOJTHXqLNDLx7FTHb7oah/J4KafaqFEVVTS6k6s07hsm2eN//wygkhgd16sq/oycMTqXE+jljsflQKRauOezyONWfn6pqxvVLdyMzQQ+7zYYj5Y1IiQ52eb7c+//p2kEiolhe34L0BL2gLOStNsARozYgWofXbhuKJ9ceEr3mQpBjbzEtMxZ/WZcLO8j6unlYXLe27y5FtLd981Ybpi2VJ39Xp0Zi5mV9MDU9xuuKn7vPi9YFwBAcACNbYXRNABT8AY+UN7oVSfFWm0vO9tHyRhwtrcNzG48R42qDDl8+OgblDSTJil58dQVBYyvRvxvdT7jfv+12PS7+M2JvR3g/p6gDIK1WWnEL70e8/ijpW/M78WvjmFmkNjPwzlXOnN/w/sDsXUCgztVKRg76RGcFMEBLbjee68pv6oSKAwZMkiSg2AkJ9cbSxso7PBMd37UrhvIvgmoib7Xh8c9zRPdRywdaNaRQqxS4vG94pw66bLUxM0GP9R2wLnn52zyX+2KCOVQ0ETEGjZpiSW1hZRMCFEAbU/568ovDLp8vJW4JYVrRbZYsHylrgLGx1cUr7807hvfYioROq0buC1OxOfccpmXG+k+gnQRvtSH3bD025ZTiwQl9kX+uGW9uySfG426seQoqGlHR2Cr7fn++fjAGx3tf8RMlhxh02DiPJIeYWiwY8dI2F6uWEkb0Ia12e0JJTbOI/FHM/ugAyh22UnlGE0a+vE2IF1QCsKFjBuF+XDj4f6XegBmrgNl7gDn7gdnZTpKm4oAZb8u/ZsrLzvavlSfVsEomeq72FDGfLtkPvNqXmDr/u59YbEETRay8uALY1gKsGu8Qakwmj3c1VBxwywrxfbyXQpDaYmKYTWEY3Lmh/JY6YOlwpzClO77veYBUJPLe/aOwfg5RBidHBSPN4KycWax2zFy5BwUVjT4LQHirDSfKG/C/PUUCgaJzeL4ur1Hm5EnJHwAEcqTMkRYjnl1rk5wNc2WED6kxIchwiGEy4kNF1RDeasPjaw4JtzMTQnFlcgQy4pzPCeSUiAsN9Ok7nW/otGrceUWSn/x1ErzVhpuW7sSMt/bg/b2nMe7VnfjjB78Ks6ZSYQ1vtaGgohHzPjkgeh+V4690ewNI23VHnhFmi+vxhbfasKug0pkcYjTh5uW7cbysHu9lF7uQPxZpBh0CA8hFK91fMhP0Iq8/FkkRQcJ+waK8QRz3yWZL0/8dKW9EodEE3mrDqUrTeROP+dEx9M5SxqUCNv3DXRszMoVUBVmfQADYtxIYM5v8v7ZYPneYNwPvXcN8Xgvx90u7Rj79g87mqdSEQAK+mU37iuYa8W2T0bvXsXOEvs4oShNRgqMIQaYoP9h937ebwQ5YD4wJwdgBkSIrkE3zxuH6JbtQ5MiLza9swtTFO30yHuatNkxftkvI2e3s8rKiFjm08nYkRwZhzSNX4vZV+2RTTQCJYbMDnEqJDXOzRC012mKz2uyiquK/byW2E4uZ+LlW3oZbV+3F5gXj/RWPixxFVU04XuH+AoYV1niyMlk/Nwu1zRZhxpTCbOEx4qWtQm7uweemgFMpheSQ21btdbEsyjc2uQilpEiL0WHpXSNw3ZJdAJz7y9pZo91us3S/OFpah1n/+xXnGuWFK9QjUoozNc348xeHO2Va7sf5gZ8A9kS0OYyWf3jO2QJ118ZUccCt/0e8+ViYSp1EJbwfIUJGV0d1F3BBQME2ICTGNf3jwa3Ee5CNgOtOJE8Qt52TJxBiWnkCaCgD+lwBnD0g37q+ZRX5682sI23vhsQCr6eIM5H1id3wxS4MOJUSnzx0BW5alo28ikZGwUoOzho1h6/mZYlaO4BvxsNFVU2y5K9vuMZtxcHT8r5553DRcHtSuBYlEhuYoupm3P72fgRwzpNMelwI2qw25BubkBwZ5DRslvkM+r1EfmrxoSI18ZNrD2HDvHHoE65FIKcU1k9ehQlbj1VggEF3XlSSfpx/8FYbHpOMTrBYde9lmDIkRriAYCt1LAZEafGntYdIdraEGO0vqhHIVEubFXtOVuM/WwtwuLQe/SKDUFzdfvv2sckpGBwXiqwBkcJsHt3nWKPvoupmlNWb292fn9t4DOca29AvMghL7hyOv2486hjnCMW/bx0KhUKBplYLbl21X3iNAkB8mLbTpuV+nB/4CWBPA5vuwYIKP+QQPQiIGQpUuA7LA3AIK7YAb40D6oqc94enArUF4ud+OI385bRA1EDi/Rc9mBAhFecwmh7unDuMHdY1ZtNyCNAQ6xc6n6jkgHcnEfUxC04LPFlIKnb6RGKTwyYyyIEKYmw8sHEeqexFDGDWu6OpUn8Wosi7mKHd9327GbyVVKyKHK0quYOz0WQRkT9AvnrmDlK1LcVT13WsYpocFSyqWuZVOGfwWDJYIElXeP32YegTrsVNy7NRVN2Me1b/3G4lQuSnVtaA1feNxEMfkhYeHZpXKRUu62f2x0QU0tE5Rz96Nkpqmj0mxAxwpKKYLTxuXpGN/ArXC6CU6CAACiEFRJpUc2VyhFBR0waoEKfXCNuiN+QvzaDD7IlOf7xUSctfavTd3v7M7gvF1c147LMcfDU/C0aTBfF6jShycf2jYwWVM9372e8iNZz2o+fAf6TqaSjOdiV/APDAt+4rWSoOeORH4JEdRNwBiFNArDyxi2HJHwBc8SAQ0V/+PfkWp/Fz5XFiL9NmJpWy+zYC4cnksbZmIO874NyR7pmNoxnFARry2VLyR5f1rXFkRu/tCa6JDFJQa52VV5LKKRXY1JwEOHqwckjo4kcATxUD05YAj+wk67mXiUB4qw1HS+uxdFs+8piTExsNR0E9xSjSDMHYONd7UmO3yxPAOZ/mYKaMIXN7oLYQWxZNwJt3DBMZMweo3L9OoVAQslstJrsUcjNKUtPbxHDXkyT7HCnk5gz96P2QzsSplUCqY16WztLxVhumL5cnfwCggAKFkvGExz/PEbY/jZrDweem4IMHLsfB56YgNSYEA2O8q5oZdAHINxJPSnf7l9Tou739OSkiSPT5RTXNuHXVXsTrNdhfVCOq8NW1iGcDjY1mUTXT21xtP84/eteZ7FJAvyxCQqSChzM/k9k8d1BxQPxwYN4vrmpV1i6Ghb4P8OheYNVEoPqE6+MsynMIuarKIz6AVsfyVRcAa+4l/48dBjy8vfsIkj4RUAUCVhlVHSW3bFpJ7DD5qqmctQ5AyN79XxNhDJ0BpOvx8j90fvkvAMiJabcozoniH9PTXZ5bVNWE124bCrvdLohDaHWD+uN5sqzwdGI5XFqPE+UNyEgM8/l70OxiFierWhAfqkZZgwUZ8SEAFA6T21DY7XYR2U2ODBYqEWxkXmZ8KNbPJeMTJTXNWDNrtGBnAUDwIkw1BAvVzXVzxiLvXCNmvrVHVA30xly3u9Dd/myXMuhMXEFFI87WtmDJtgIcKWvAQINOmKU7VWkSWqxykJtNzS1rwK6CSoxPjQanUkKj5gQLIgDYODcL05dnOzKZlSJTcqYnAaOJzOjJVfSl24W3rVhOpcSXs8fg2sU7UepoJ+dVmDDlzR04W2cWMsMT9WoUVzVhSFwIjpU3IjNBjzH9IzsVW+jH+YPC7u6SnUFDQwP0ej3q6+sRGuqqDvKji9FqApZfCTSedd73yE4gfljH3o8VdFAo1cDTZ5zpIXnfim1kVGrAanF9r/Yw70D3meEaT5CqnRx0fQDTGfF905bIEzepuXbsMOJr6Ks3Yg8Fe9AvqWnGpDd2uH0ubVsCwC0rsoWZN9bOQW5A3R0JZMlVoEqBVqsdgZwCrY78KwWA3Bem+qRKPVVp8vgd1CoFfvsrWSY6NH/7qr2CeITO7NG5q6KqJtFc4er7RmLJtkIy3xQfijfvHC4ivmxbLzM+FGtnj8Htb+8TRCL9IwKx9J6RCAzgLsgcoKecXz+6FtJtcfsTE9E/Wif6DdIMOrx++zCU1rXgX98ex+ka0tEJUCrQJjMiwY4OmC089pyshiFEjcAATvDXi9drcKa2RbgIaW3j8ehHB1DGKHPVKgUOv3CNsG/KbRcAvLpQ4K020fEAEJNOOdx7RSJuvSwRQ/uEe/05fnQ9fOFrvf9sdzEiUAfc+7lY2MF10G6CChx+vwF4Pc1ZPYtOIzN1FLvecP4/vL9T5SsHtgLIwl3Fratg89BibpbxJBx0nfxzaV5yR02xezCkB/01s0YjPS5EtgIIkLblT3lGlNeZRQd7auegUirwi2RAfX9RjahSwYKmkVAiVlZvxp6TVfjrhqMAyAnkv3tOY/ZVA7w+MbDq5cwEPeZfnYJHPnLaa1isdhwoqUNWShQA4Exti0g5TKt0chm4AIQ5P4BUZVjlc1m9WdTWyy1rwP6iGpFC+FRNKxZ9loOCqmahong+T3rsvJZ/6L57wW6LbHWLUymxZtZo7DlZjTi9Bn3CtbjznX0ilawc+QOcowPxeg2G/30LzGxVmdmeUmNCYLbwwiyfFBarHfuLapCVEgXeasPGnDLRdlFU1SRU0umxwZ15c1FVk4vquL1K0cc/n8XHP5/FkLgQbJo3zr8N9gJcHGe9ixHRg5xii7jhHRMesJW/iAHi1mnFEaD0N6AyD4jLFLdEWfIXPRBos4jnBx/eTv7WlQC6aMBUCYT1IcvcWSLlyZRaKXlvlqjaZKwKlgwDnjotb26t4nqllYsUZguPvaeqEafXIsWgcyEDZfVmbJw3DnnnGnHgdA20nBL//j4PlY7EAAXEBIjF/E9/Ewxh6dW/NkCFK5MjPC4T22rqH61DqEYpEEAAeH1LPn44VuF1pYqNh0qKCJIxrQaidIFCq3twTLDQotIGKDEgWkfMepkTdnsxc5RISXNW0wzBGJkUhrQYnYgYFlQRYplb1oCf8oy4aqDhvJFAqb2PN0P3phaL3yC6A5Bui/Q3Nlt43Lxst5Av3S8yyMUiZWiiHh8+MArfHqnA//YU4ZjDVoaODmQXVonIHyDO4aZzhp5azfe//wsy4kNxstIkahlrA5Q4Xd0kOjYIohAvqsYp0UE4U2t2EUDJ4Vh5o/8ipJfA3wLuyehs+kRVIRFGtAsFoOAAuwyJuuNj8nf738l8Xdxw4KFt3VMxYxXQAVqiAGbJG9u6jR4M/OFr4I1B8uSP4t51vuUH9yKwrVmAtG2/eHSsU6GXqBcNfHvyJ5MiNTrYZW7p1ZmZmD483qeget5qww1LdiJfJlmAts98Bdtm7hehRXAg57bCCQDv/n4kVEoFxg6IFLXHiqqa8PjnOWQWMCEU/5o5FLet2uvS6qamvgs/O4h8YxOGJujxyUNXYOZbe5FvNCHVEOySnHA+FcG81YZCownzP/kNBZVN7VYh65rMGP7SNgAda8n7QUBJ9LXpBtz5zn6XqhwdPdAGKLF+ThZSHGphwLn9ARCNG7hUABNCBbN2afs51aBDUaUJfLtncCeETGtDsGiflO6L7D6WZgjGJkcOMFXVp0YH4/GpA2G121Fe14KXv3XOkNMKoL/1e2HgbwFfLOhsuDxriByZBlTnu3miXZ78KdVOgYcQLCkTMNlVYBXQbS3kNkveVBxw3waSQlJ5HPjgJs/kT6UmlcSLFKx3GEDatmX1ZtkKBSBuFVKw83kU/7glHbcMi8fd7+4XWqlpMTqfyR/9TDnyJzXO9WVeiG0zW3ibYHIrB7VKgYf/RyqcLDGiLbX1jBF0SU2zi3qR3s+plML3OFxaD6PJgm8Wjhdad1L7j9zz1IqVI/W5ZQ34aN9p3HV5H5ffi7faMG2ZM5vWDuCrw+W4+8q+8MN71DWZhfi1v6xzbY9mJujx6UNX4EBJnaxwSi6zW6PmkPP8VNEMIDtTKq706rCEMXhmkRSuEcXAsaBVwRpTK4bE6nDsnEm2aszuY+x+ueXxibL76u2jEvDh3hJMTItCekKYn/z1Evh/pYsZKo744M3ZD1i8jOEK7w88tB24/nXAxopAHIc4moTRHeiXRSp/APkrJW9tZuCNNKC+hNw2HgOUAfLvpQwA/lTgPtv4IgD1DqPIiA8Rqf2kB2HWwiQzIRRbFk3A/qcnIUByFPjrhqO4e/XPWDt7DL6el4V+EVrkV3i2mXAHmuZBkR4Xgi2LJgiVSbOFx7SluzDpjR0+2cRwKiWSIoLw5BduvC8BcEoyF0VB22nS96HrSmoDY9CpMW0JWbbHP88RrEAyE0JF61mj5rBpbhbSop1G1+4UwZ7ivjoCOVIPAH/76hiGvvg9jpbWw2zhBcuboqomlNaJbaaGdkCVfSmDt9owbelugfTZASSEOY8zqYZgrJ01GjqtGlkpUThT2+J1pKJGzWHS4BhkJIYjNSZEtA/T9vP2JyZi84LxSDHoRPsWQC541JzraV26j1c18zh2zoR+4YGCKbx0+eSOI+6OLWHBGiyYkoZhSRF+8teL4K8Adic628LtCqg4MjvXWOr6WHgKYGkAmhwRa5yGzNT9dxrx1nOH9bO6pw0sNX6WkrdTOwGemWNUBrivAD6yA9CGde3y9TBQ7zB2BtDTwVdufulUpQltMuel3NJ6nKltwRNrDqHYoWLsiMCAVhKk7S4ALjNNvr5/SU2z2zm+hDANVt5zGZ7dcEQYZqfEzRPeuIMo7eNCA4V2F0DIY79I8trWNht4q020rjVqDt88NkH2e1KQlv0WtLSRtuDB56b6XFGVIikiSJTywMJiBaYt2w0Np4SZtyEzQY82Xkw8B8cEYWBsiMtr/XCPkppmlNaLraievWEw5nxC5qgLjE04U9uC5KhgoY0KtD8W4E0lXGrlsnb2GGH2sF+EFivuHYlpy8TxcKnRQSiolPenLK4l38MvHro04afq3QUqwFg+kvztDpNkb0FbwQAQkgjMeJekfNQWEvIXNRC461On96An8gcQYYqcwXJXgDV+ZmHlgR//Ib7P1gZEyQg5DEOIeOUSgEbN4epBMRgUF+p1+5S9go/Xa4QqItvcp2bQLLHwJRFE+pmpMSEuFY2SmuZOvb/UtJoufyCnRJiGw/SVe2Bps+LrecRIms5SyYG2Uqcu3onHP8/BTMZEmoImMuQbTbh5RbZsxUTuewJkXuyVb48LLbiWNhvWHjjrc0VVCk6lxMZ5WUjzYBpMZ8pyS+txQpJnu/SeUf6KjY+QVrWHxIVg0iCD6L45H/+GQqNJpEb3ZBRuarFgyhs/YdIbOzBDZttyh7J6syA8Ka5pgZpTClXsNIMO3y0cj6/mj3epFEqREd/+xZEfFx/8e353gTVfdpdIcb5AW8HzDgCPHQISLhMbJlflkUQQShJZTHgaiJH6DypIbu75RHWhfAqIAsDsbCJOAQDDYODh3pfW0RlIUy3kUi7coaze6dpPW1qBnAKfPnSFEMMGkNQQXxJBvAHbcu3I+3MqJV67bahwmy5/K28T8ojzK5vwxNrD7frzSWPg3CU6UORXmFzUyO5garEg88Ut+HBviej+5zYexQ1Ldna6HaxRc/hmwXi88zt5wZeGI9RY2gaMD1X7nM98sYG32nCivAE/nqjw+nfgVEp8+vCVeOWWDHw9Lwub5o2DRs3h38y2WGA0Yf6nv4kSRNIMOlmFttnCY+TL24RKe25Zg9fblnRsITkqWGgTf7NwPAbFhUKj5rB+bhYZvZg9Bgad69jME9ek+S8ELkFcOmfJ89WOpZ+jT3QKMOJHdK8/njdgBSXh/UhUHLV+oTYzD24FKk8AqyeTaiCnAcY/BmTcIjFgtpO0jJ6grq08QexoshY5s4AvIUhTLdbOHiOogAmpIlUv2loCxAatUpsTAGjlia/exIEGt4KSroA7Sw1f4M7EgBW35FWY2m1v0UooJcNULRmgUqDNKv8ZcpBr423OPefWQy3f2ITpK7KxecH4Tq1fTqWEXfIpL01Px2VJ4bj1rT0A7C6t/gfH9XdpZV9KkJody7Xl5RJwzBYel7+yXaQW51RKmCUruMDYhC2LJsBqs2PBZweRX2HC9Ut24at5WSLV9f6iGq/sVeTgbh+Sbuus6GTP01Nw8HQN7l69H7yNWDuN6R/Zoc/3o3fj0iCArB9e/AhCdLqDBEo/54HvgPqzF3YGUA6ejJBjM4h3HjuHF5kCxA4HzuWQ58gJNLxFR4l4ZAqxfqk8Lr6f0wKf3S25T+Pe/+8iQ1FVk9Bmyi1rwO7CKqGSlVdhwk3LdkOhVCC/wkTapXY7cssaRMkANpv45BOoUsDC22C28NCoOZ/mgryNjKPwJZ7K3evl0Mrb0S8yCMXVzV5FtLGVUIC0aFffNxLROjUWfn4IxdXN6BcZBJXdhpM1ZmQmhLpUz9wlckzLjMVf1uW6JYGUoFLFcUfIsNnC47HPc4TbGk6B20cmYn9RjYu3HMVL35zA61sKPCa7XMwoqWkWmR23tNlEJueszVIgp8SBZydDp1WL1PfUGD0rJQrzPhXHSyboNUiOCiZKeEdFuai6GSNf3oZDzzuJJhFzOaPe0uNCfKrM+roPcSolLu8fhSN/u9anfdWPiw8X/69u5YFTO1zbsR21V2k1AUfWAxkzSGIHC2nbt/5s98WidRaejJDpHB773Ie3kWpbQxmQPKFj5KozRJyS1tf6i4UgcvOKvNnVQqYzYKu6tJXfQ9ND/vXNMZFJMevlx1b56NB3a5tV5KE3d2J//N+eYjz8vwOik5438CUyrquQHBWM1KggwYiZIjNBLxBbL6xOEa/XIM2gQz4zkzjv04NCVSdA5ZwBTDUE47XbnGMRtOpntdllEzl0WjX2Pj0RN/wnGzUtpM2YGKaFzW5DWX0rhibqEa/XdCrOjZASJ9Fbfs9l0Kg5QSkuNSWmaC/Z5WJGUkQQMuJDRRVA1uR876lqYb218jZMW7oL2/50tWidUmP0kppmF3X15gVZQpU9OTJImClt5cVEk4i5pnot5uoqSLOH/bj0cHHV/q08MT+mggtqHPzxTKe9SGfasa0m4J+JwFfzyN9WyZwQK7boCW3froSKI9XBtGs6Xlnr7Fxk7Wkx+XMHVWDX+f9ZeeDdyUTM868k0gpfeSWJ6WuT99o6n0iOCkaqwVktKKxqweu3DUOgjBVEWozTNoL68JXXi7/Dih2nBNLTytsw+c0dMLV4lwktVxnpbnAqJZbec5novvf/MAqv3TZUILZHJDNVcjOTd7y9D/lGk6D0BSBq6bH8qcDYhOuW7ML0Zbvw0d5iTF++G5Pe2IFFIqsYZ9XRbOFx9eu7BfIHAGfrWlBW34pAToFPHrwCZ2pbXMijL2AtgbQBKoxzxOJRpfj7fxiFlCity+u8SXZpD3R9snYzvQGcSokNc7Pw3cLxeP8Po1zav3F68fo6XWvGml9I3vjB56bggwcuFy5y2Fm8xDAtcp6bjLBgjfA5X83LEvZJuXXuq5jLDz+6Aj2vhNFRyLVfT/7onHNrawFuXAIMu6vjlZtDnwOs+9Ohz4GR94tbmg9ubb/FSStKIbFkli7pSjK/1tNaxV0N1pjaV4LcZgZWT/LuuX/Y3PEKJf3tAIfw5Kiz9c1G6VWeAN69Gpi164L9ZrTytPiOYbhxudPct6LBNbJJbh6QUylJOobDJkQOFY0Wl5aVO8hVRs4HUmNChDnGzAQ9xqdGux2il2vTsgKQ4upmWXNsORw9Z8JfNzoj7o6UNSA12kEgHVVHmsnqrgLXytux91QN/v29U5RF/Rx9ASV6ci09Si7G9I/ETUt3CdXSfpFB+HpeVqeqtOz6pMkXHalgXihwKiUGxYViUJyrSjbFoENKdDAKmSr6MxuO4PmNR/DrXyeLqmftzbPqtGocen6qv+XqR49C790KpbNk0urSW2OBmpPi13y9EDjwfsc97HSScnlQpJh0/n4DcHgtYLMDl90DQOOcs9MnErKXOBJ4/3piYiwkrDr+hiYCU18BBl17cc6veUuQ5VCc7bSpAQBdLGA6J//cc0eBPpf7tmxtZuDdqwDjcaeiuDzH82uMx8h4Qf+J540E1jWZ8eHeEozpH4Fn1ueioLIZQ2KCBdKiDVDCJml5vnf/KExIi5YdENeoOex7+mrcsHQ3yhzeZoNjglBhakONIy+YbVl58irzRELcQToz6GsqCODwGpScfJOjgpEZHyrEvNGZqqKqJpdKm1QI44789Q3XQKVS4lSV++oc9VvLLWtAodGEP39xuN3ovb9/fQxnap3tQ3MbMWzuE64V0ki8WRfttfQ0ag4rfz8KUxfvBEDIrtFk6VQMHEue6UXHxeIpx6mU2DBnLKYtz8ZpxhKItwMjX9qGo3+/VrSNtzeL52+5+tHT0DsJoNwsmT6RDP9TkiAlfxTlOaR6E5vh++emTgVUGsBqBhQBxEOPJZ2vMnFK3z8JxAwFKmhSASV7LOzivw1ngS/vA8ABz5ZevCSwI3ORNCWkrQWAgpA/LlC+JTz0Nt/e28qTap7RITDxRPyiBpKfsjKPbG8fzySK6oe6SVjEoKqxGaNe/tHl/mOMt1tLmw2zPvpNGCrPTAgVkT8KSrQMOjVuWb5HIH8AwNsUAvkDiKDgyuQItyIHFr6c5NjsUw2nxK/PTsY9q3/u0Bwcb7XhjCOWjb7mzTuHA3CaMvNWGxZJhBL0+W/eMUwgRhQZ8aGwWG3IrzAhOTIIS+4agZtXZHtcDnYecfZHB4S5QU9gyR8AFFY2YerinUI1tSsratTah2ZFd9b7LV6vcamYphmCLwpPOd5qwz2rfxaRPworgN2FVZgy5DzbYfnhRxei59fo5SA3S1ZbLK4QecKXD3bMmDlAA/z5JBCWTLJzv30Sgv2sXCSZQP4AV/LnCTxQ6Hqiv+TQZgYKtpG/NCXkpuUQ1iXfStr6LCLTCDHzBbXFjoqsA9GDnFVAFg9uBWbtBJSBjs93bG/dGY/nAG+14brF7jNvpWhps+GDBy4XzI/ZOa2CikbMWJGNSW/sIP5jEgLCCkcA55bLVns6Mqcmxe7CKqH1bOZt2JBT1qH3p+KT+9//BSNe2orDZ+pww5JdmLp4J55Y4/SOlKo+zbxdIF+s52Fmgh5bFk3AhrlZ2DQ3CwMNOhRVN+OJtTkel2NIrA5/us4prCLtZHKIHejBqNkdaNvY07rwxfMREMeJ0Tg+XyCNsjtT2+JSMQ3gVHIv7XVwF7Pnhx8XC3onAZQTW7SXdRve3/n/yhO+CxCowKT+LFBXxDxgByb8RT6SLEoujcJBGMNTAYWH1ovC/UMXHaTiHYAIbF5NIhW2f/dzksDhd4t/+2F3idNAqvOBAx8A5454T/LZ7ckwhMTIPbQNuGet+HnmBrLtiIj9+UHeuUZUNXv+PmkxOqTHEa+vzAQ9slKiBPI3c+UeTHpjB0a8tBVTF+8UrGOks4Lp8aHoFy4efm/l7dhfVONiOtvVVR5DiEZI9vDGuoVCKj65eUW2oOZlyVNSRJDbxAyWGK2fM1ZI8yirNwtpJQVG5zFGaqoMAKeqmjHro99E97XyhIhvnJvlQgL/er2rCj/VoBOEJJQ8ulvX7O/qTY4yJW+81SakwfhCIE0tFgz7+xaBaLszTj4qk7ncG8Fu74ESThuggCC08cOP3ore0QKWzvtJZ8lsPJmrkyJ6IGnVxQ0Hpr0JvH8dYLWQVqIvhsFsyzluBBDaF2g47XzcMASIHSZOqojJJG3BUzuBT2933q9QAXYeqC0Qf8bkl4Ef/0aIZIAWGHC198vXmyHXzgeAVeOc7d22Fqeti9wc4W3vAavGOt/zm8fJ39jhxL6mvdasu9nEAZOcopUALSGjcttN7HBiC9NN4K02zPlYTCzkBgqW3jUCT3xOKuM2q1N0wFYy3IkRAGD1fSNx1UCDI5lgq1DZ0XDEHqMzxs1y/oDjUqKEVrU2QImxAyKwdDv5TG+sWyg8WZ0EckohfYFTKbFpbhZuXk4IYmaCXuS3JjfDRUmAtBLUN0KLwipx5VROSJMRHyoQ8Y1zs4Ts44z4ULz6/QmX5xcYTfh2wTg89lkO8owmDDTosOaR0bLrWq4i624GjbXo0XBkZjI5Khi3r9ormIivn+s5Ku+mZdnCBUNLmxV7T1UjMTwI6XEhIishX8h7Twa7vcfrNThe3ogvfjuDMclRmDLE4Bdy+NHr0fO3YHcEgW25Fe0UKzQBkFOkoy1bcVSsIKWEwtvhfbblXH4QuPU94Ms/Oh+PSgEe3u5IpTgDhPUhbUQVB6QwJEKlJgRUisg04Io/AFEDyGIPuPrinP+Tg7SdX10IGE8AtUyVVRFAlNKserr2NCFjKo4QfTbZhOJcjveej3KziZQYntpByB9AKsDC44HAgz8AMRndOv9XUtOM05KKih0QmcdmJuhhYSLQjp4zodBowqC4UBGJcUeUMhNCcdVAAziV0qFYvAa7C6sAAKOTI0RiBF+H+1nyEaBS4JdnJiEsWCP4n1FiWFztbNFS6xaaXuAJVHyy91Q1Xv/uhLAOAFKBO1PbApVSgaSIIBKbtnC81ySWkoCiqiY8/nkOcssaMDAmBHkVjR5fx353gBCosnozNs7LQlm9Gaerm/DAf391ef7QRD04lVKoOuYZTSirN8uuc/Z3ba8iy3ramXkbrl+6W+QXmSuzvlnSXlZvRhGzDQZySrzxfR6OlDciMz4U3y0cDwBQKRXtxu71JrDb+4i+4RjRN/wCL5EffnQdej4BlBKE/C3Azn85B/XjhgM3LZV5oR2oPEL+K9ee/Xim92bErH1J7HBg41znYzFDCQGhPnlScYkciZBi+O+AV/uR5aQpFpcK2HUbNwJY94hr5q+9DXh/GmBtIRVdWv8K0JK5wAANqbZW5hGiT2fzYod33otRxZELBcMQ8ZwgAIQndzv5A5ytS2k+bUubDe/8biSSo4PRJ1yLayUihrO1zYKvGFvJOFnZhJlv7UErb4M2QIX1c8a6mM9q1BymDIklUXMr9yC3tB5pMTpsmitvG2K28Nhzshpxeo3QPqXYVVAlkI82qx2Xv7wNB5+bCp1WLYhGeKsNCz476PK+3oJanYxPjUah0SREb2Um6LHos4MCUaFVLk8kVlqtpDFaa2ePwf6iGoxMChPEKu2hsKoZBRWN+PMXh0WVtni9BhpOATMrnogOFqp93hA7XyqyUk87AG7zjnmrjcyJrtwjCHQ+fuhy9AvXori2BcmRQVh+z2WYtmw3AEIeKxrMQqXTDz/86B3o+QRQnwgo1YDNUTn7/C7x4+U5gKkCiBkGVBxyeblHeJsKwrYIq/LF0WNXP+Ndi5FVsSoCSBuYNvG2Pe98Lm8mbeOB1/j2XXor2HVr4yWZwwyoFx8AYb21tQAntwODbnAQ8HRCnot2EsseZQCpKHY2tUPFAQ//CLxzlTiKrupE51JlvARtXV77n504XSNuO772/XF8+9hEUiWUiDliQwNF79E/WgfeasPT63LRytuQHBnkkksqRVFVk2CPkl9hws3Ls/HNQnFuLavmBcgcIfUc5K02vPqNmDi32YCblmdjy+MThfdh47IAYEBUEM7WNqNPuNanVhv1dftmAanyWXgbrltCxDNslcud3Yy7NBNqFk0zlr+cPQblDa147LODQvszIz4Ej01Jw0MfHhAt09naFlFcX1FVE5KjgjEgWidqneZXNgnVvjWzRgsk1BOp8rYi2y8yCAFKiPKAWfVuRnwo+oRrUVDRiEWf50jEMjbcumo/AECtUmD9nDEuytj73/+lV/n/+eGHHz1dBGLlgYKtTvInB1Ug8MntgFIJ3PmZb+8fNYgQTKkAQfZzHC3CsCTx/WF9vPus2mKHhQlIRcuTKthu7ZhKubeCrtvIFNI6Fz0WKP8aiq1/E6+rAA2Z3fvqMTIXuPJK4O2rO78+AzTA7N3A7D0kkxg4r2kvGjWHt38/yuX+gspmp5ddvNjM9vE1h1wG9dm5sSKHD5wvyDeaXAb8pXmzR5nkjaKqJhRWu8b1FVU3I7uwCrzVBt5qQ4vFKtI9naxqxgP//dWj2MATKDFSKV3VVJ7EE+7STNj1lldhwq1v7UWfcC1ev30YVt83Epvnj8PiO0dgXEqUIOIACCmM04u3YUo+WfIHEKVwUkSQQDbvf/8X3PH2vi5J1iirN4vIX2KYVqTefWxKKm5ftRdTF+8UkT8pLFY7pi3NxvSVe10e6wpluB9++HH+0HMrgOzsnxyufRXYsxRoLCW3yw8C2190Ph41iFRoBMiMzVcVAP93DWk5etsOjh7kFHzEDnMlLO5g8/IkpgoEPrvL96zciwEqjihwaaUterC44iaHqjzXKlxtsXge0HgYqDgCxA/v/PLFphMi2BEz604ixaBDRlwIjogG7kOFKtb6uVnYmV+JP35AZsvyjU2Yvjwbm5mKnS9zYwCxR2E/U27A/8rkCKhVClis7Qs3EsI0KK0zQxugwv3v/0JUv3a7UCGTorNZtclRwaKUkOSoYI/iCXdpJkkRQRgYo0NehXM276bl2ShwzOrR7z80QY/PHr4S+xzEcVxKlIvPH82HZXNoAzklvpw9BpxKiVOVJq/FHd6C/d1pBZO2sbUBKpeqJUV0MIfKJvGxq1QSH0gxMMb3BBM//PDjwkFh90Ju19DQAL1ej/r6eoSGukbmdAuqCkn+qhwUAY4qGoOIAWLz5we+B96/1rfPnHfAu3aeVJXc3nPLDwE5nwG/vuP+eeH9gakvAWvu9X15LjbQ9atPJMptdxcBgDxRbjMDS4aJk0KmvgK0VAPNtcDkZ4Hg3mnhwFtJSgRvtQmJF2zLjbeS0Po8pp26/YmJIgLha9oG/UwAbgf8TS0W3LhsN4prWpCZECryH6QzhJkJeqx1tDbvf/8Xr74v24btKKTfV2Ronah38cOTUyzTubiFnx5EfmUT0gzByDfKW0+lGoIFy5iM+FB8MXuMU23LrJuCikaR+TT9ndpbvq5YDwCpzp6tbXYRo9DZxH6RQSIja4MuAEaTzDw1CIE98OzkTqWK+OGHH52HL3yt5xLANjOwfBRQf8a75xsygdpC0mblAoHACKCp3PvP646Km5UH3rna1TdOWp28dx0RGgCuiudLqQIoByvvXkAT3h+Ys1esmG6vckzx5MleSwLbg9nCC3YjXUkg2gObLnKgpM5tvBtLcNJidFArFaKqJsWrMzMxfXh8t9ht+EKC2eXNiA/F41PTEBOqEUQQ7WHLoglC5ZH9PE9EryOReN7AbOGx91S1oODNiAtBQWWTyA/yy9lXYuFnh3DWUaltabMiLUaH128bhgWfHkSxmzbvBw9cLhKCdNd38MMPP9yj9xNAb0/iviDIADQbmTuUAGxERTzj7c4LBeQgV8W8cg4w5QVnZUtK9HypLl4qaDMD/0qSsfqBa5VUus4n/AX4+W3AXCt+3eWzgWmvAi11wL53gLQpQOzQi2adX6iTrzsRhRSmFgtuWpaNoppmZCboMe/qASIT5TSDzkVs4u3ne5tF7O06OlVpwqQ3dojuy0zQo8VskZ1vlGLLoglu7VU4lbLLq7HuwP427pBq0OF0dZOonf/PGRn4354iIXIwQKVAm9WOQJUCrY7nSWPrAOCWFdk4UtaA9LgQ/OeuEReVPYwffvRU+MLXeubZrvKEmPwpOIdqlkF4MvGK47QA30KIHN/qfmbMJR7MBty9lvj0dddJP7yfJA8YwFVPkYqVnPEw0PGs3IsZtcVi8qeLB0xl8jYvIluZ4cDxja7kDwAyZhDyR/Obd7xMCODDP3bL9nC+CVlH/Pq6AnIiCun8Hm+14dZVewVfudzSejy38ajweKohGJvmZQEg5MvbdeYt+YSVB199Crd9Vo6cMlO76lU5M+jc0nq8+/uRePh/rrNz6XEhsPBWFFQ2IzMhVGQ2LbeM0t/J3bbCttMBtGveLAXrBegOrZY2EflTqxR4ev0R0XParHZwCkChIHPVASqFS2ydhbcJ841Hy0mr268S9sOPnoWeRwDbzMDqKeL7pOQPcBryRg0EZjoqeLwZWDlavm3cUOJ6n93zwbDTUHHAIz+SGcCCbcDoRwBtmPMxP9HrGExl5G/lcSKukRJob2xlArTAz6vF95073C22LlWNzbhhSTaMJgvS40Kwcd64i/YkKCeikFatSmqaRTOKAGBsdBL8lfeOBKdSOtujXhIHb8gn7S5wZQfxoq0/ZuLFdoUWcmbQQxP1GJ8aJRJyULx++zBhWSn5o0S2vWUUtYUl37ukplkgfwCxlPFWIELU1u2LdErqWhHIKdHK2xDgQdjD2wHe0TZus9qFqiAVFu0qqHR5zeHSeq/Nvf3ww4/uR88jgMXZpKLHQqUhalvWC46aO5/LAZSOr/HfaYT8KQPkzZ+lOB9qWxUHJI4k//zwDtI2eGSKfNKHtVXeM5GSayvvrAZKsX4W8IevgR9fct4XO7TLbV3qmswY9fKPwu2j5Y04WlqHYUkRXfo5PQU0lYNtcU5fkY2jDpKUGR+KTx++EsmRQSiqdp0lSwzTtKvUdQd3Cl4RGGP5YcpTSFIYoYsf1K56lZpBr5+bJarObZib5WI8TU2fhybosWbWaME/cGiCHp88dIXHZZR+b5YwJUUECYpmwKkABzxXmFlSybZt3SElOhh/unYgDCHezzm2We3CzCanUmJM/0ghqYb1X3jss4MX9QWQH370JvQ8Atgvy9nWpbCagVtWAjVFYpUsABgGE7VowQ/OdBBvyB+Ft2bQfpwfiHKXhzvnMx/aSkyd19xHrF8o9Anu34tWA6sLCeGj2wdAqoctdcQ4uhtnAD/c61p53pFXddESQABCugcAHC+rF8gfQKpWt67ai6LqZqHSxCIkUAXeaoOFtwnpJ97Y1dDPZckn2/4VCFJYEvjoYdBUHsIhW3+U2A1YPTXV6+8mba1LjaetNrug7D1cWo+9p6pFhM5osgjLmB6vw8acMkzLjBXUswadWkSYHv3oAL6eP05IJFnvqEQCzhlAT1VDQEwqW612rLr3Mvxt0xGca5T3gDxa3ojEcOJHmBodhILKZkEZPDhWh2qTxcU/UhugwlPrcvHx/hKsmzNWFPMXFKDE7e/sF96bRhT64YcfFxY97zKMxnpJYWsjiR8s9H3ICf6968TpHL7gPJr5XhKw8iTLt2Q/8NO/CcnyBaLc5RzSwv0/x0iAYRAwa6fTezFuOInh8wQVR1730DZ5E2dtGHD1n4GEy7qlCnyVjH/dfWOTZJ7Z80CtTwoqGjtsRlxaJ67mx+sDhfYvJX/JEU6j5OMVTbhp2S5ct2QX8itMSDPohHg0dzBbeOzIM8Js4QXyKSV/gvHzqp+BB3/A7LB3MNPyIjguAA/891cXQ2j6ulOVJq+/u4W3obiqCRlxpGKXmaDHa9851f7aACWJgFNzGJkUhstf/hFPrctF5otbYGohhOpASZ3IrbSwsgnTV2QLy0ArkWzcXlFVk4hkUoNtCqlJ+Os/5LklfwAxr170eQ6uX7obBZXN6Bumwb6nJ2HLogngbXAhf/+ckSGaASyqasKpShM4lRJZKVF48stc0fPL69sXzvjRfTC1WPD5zyWoazK7bN++bvOXAqoam/H3TUdR1XjxmZz3vAogQE7wccPFFZvVU10TQQLDyCygtDUohUoNWCWvjRkK3Ppu96h/L1VYeTK/yf4eP71Mqmx09rE9sCIOCrZKG6ABZmf7rpS+QCbOQ+JDMSQmGMcqmhAVpMJ3iyYgLFgqSOp54K02QcUJ+CY4oNW2eL0G/9mSL9yvVikQGqhCmeT5RTVidXdBpfNAm280CfFocvBG+CFtq5Y18vjPnFtx/ZJdQhta2mZur6om/b63LN8tWNloApT4buF4WG12UQu1pc2GY2UNaGzlUVLTLBA9O4DNuedw5xVJjjY2aZ1S5FWY3LbAeasNj69xRmBqOKVLLBunUuK124cJkXiFlfL+hRT3XtlXJPw4XWfGHW/vw4p7RwrG1xSphmDMGJGAT38+I9jkzP3kN1K5TdDj1VszRV6CgRxpD/txYVDXZMaIl7bBDuCpdeQ+VrlNt/mBBh2+fJTEHQK+K84vFlQ1NgsjPO/tKcavz16NqJCLx+y8Z/6iKo5UbG770HmfXBycMReoLSG+eu7AaYDHjwORzHOiBgEPbSGVIT/56zpIEzgospd5/x60bTtnP5n7A1yrtHTGryO/XWde2wFwKiU2LZiA7U9MxL5nr+kVBw/easOugkqRuIEKDrx5La22TV+RLfL3+8+dw3HC2P579I9wEuRATgmDzr25sLvoNhZUxQsAQxP1iNdrsL+oRjSDGMgpERHECZVEuRlEdyipaRZ9T3ObDWdrm7Ho8xyX585ctRf3v/8LXt58TIi+UwCYlhkLAELr9O3fXYZAjhyetQEqWHibbFVGKgyhkXzsMvNWG578wulEMDjW8yzl0+uPQK0SR+jlG5tgtdmhCXCeMlKitPhqHmlPr5szFl/Py0KtqVXIdD5cWo+FnzqPB30jgnDg2cnd4uvohytMLRZ8su80DpXUoKCiEWYLjxuXZbuEkNJtRRR3aDRh5MvbMHXxTkxdvBMzmCr0pYSVPxZ5vN3b0XP3RBUHtMjYd0jx6e3ivNjw/sDt75PKXsl+MlNYWwxUM8bLVSdI5dA/99e1CIkFVAGAVTKDmfMJcPXTvlXrDIPIKMBF4Il4oSxZOgK28sUizaDzag5Pmpk70KBDntGEzIRQLN5a4NUynKlzRo218sQyZvMCeU9Ab4QfVMVLq5JUlMGilbfhyn/+iFbeBm2ACnv/cpUQ/UZnEOWEFrzVBqvNjpQoLQqrxK3NfEm1jIWZJ7N49S1tohlA3moTKp60Rd7SZsV1S3bJViJZYUhGfCgUgKBSpr+XlCT++bpBLukfUlisdiTqA3G2nlSAFACaW9tgZiqT/7lrhEDmzBYet6zMBjvS2S9Ci3ym2hioUvjJ33mCqcWCjBe3iO7rFxmEs8y+RcFu333DNDjteA47n5tb1oAP9hRDpVQgTq/BxLToS+K3nHN1Mt7bUyzcfmRi3wu3MN2Anv0LDr0N2PwYXDJ8pWA94u7+lJAHAEidLP/86MH+ub+uBhVvSMkfQGxbqgsJKa8uJPd503r3W+Wcd7AEjiI1Ogib5nnX/pVmDa95ZDTK6s0icUR7aJMUGjy1QD0JP1hQEs7m7ErBEq7pK/bgdE0L+oZr8eEDo4jS99ODyDc6fQN5q01IXMmID0VKtAKFlc3IiA/BuJQot0pngMwDXjUwWnZW8XBpPTLjQ10sZtyqoR1e/goAa2ePQVm9WURSpb/JmP6Rwm1PkXY2OKuAdgC7C6tFjxsbLYLFz0Mf/AKJngfBmgCkGZTC++dXNnVJrrEf7WNz7jmX+4oZ4VVimBYb5o5Gg9kmXCjc9tYegfzJ4aXNTo/dQJUCh1645qIngVEhQdj39ETcsCQbNc08Hv7goE/emz0dPfvXC9QBT58FDn0O7H8bqM4TP05n+wK0JAIufgQhFlKwNiLRg4FHfurVFaUeidpiwOjGhBsAvnyY/FY0Ai9uOGnz+3+HHgWp1QgAqAM4rw94bLWNkhCab0sraizUHrzmKJIjgxCvdz83yaqO24OcqbMUgZwSp2tINe90bQsuf3mbiJRSocO8T35DnqPKd6SsAVsWTYBKqRC+9/o5YzDq5W0CMYrTB6KqsRVtNkAugIkl37llDUiLDhY9rnG0w1lz7JKaZuQ6SGJuWYPsvKTcb8JWRG9btdfFyxAAyurNwu+jDVDivjFJWPFjIVqtdgQogaGJIS650yyOljXg2wXj8NhnOUIsoTdVZD86j2mZsXhqXa7L/a28TRTZF+WwZDxVaZKNY3SHVqtd3mfzIkSj2Y6aZuJFnFvWcFF5WfZ8GhuoA654EJizh8yFxQ4j90cNAv5UQHJ0/1RIIsHc+fmpONJOnHeAiAACev4Qfq+DPtGpsFU4foNwxl6j4rA4/7g8h5BGP3oUOJUS/5qZKbrvqOOg58t7UBJCFYWcSomNc7OQLCEA7ZG/AJUCRdXNuOPtfV02g/TqrZlYde9lGBDlSkb6hmvx1HViZbm0Ijkwhnw3lvgMjNEhOSoY/aN1gjXLfe/9Ct4G0LG5QJVSeC8zb3eZV2RnFZMjg0XtU/IaG2a8tZeomR2qZel8ozuCRX8TSuTZ2wrZVxDQ38dut4NTKjHAoBPWSdarP7klfwBRQafGhGDzwvHY/sTE85ZJ7Qeg06px5IWpeOWWDKybPRppjm12aKJelNdMkRQRhNRo78k5p4C8z+ZFCOlxp7WdNJ3ehN5TfqFzYQ9vJ8RBn+g+T9fd6/3txO6BlSdWPDSGjya31HqY+Yob3rva8L0go9ls4bH3VDXi9FqkGHQdPtkaG923gQDvYu3kVLQaNYdvHxuPm5fvdtt2lKLNQUC8NYNub7nZKDUAGBAVhDarHSW1zorf378WV7I5BUm+AIC0GB02OlpAtJI4MCYEX84eLVonrDULJX3FNS1QqwCLFVCrgGGJoSioIFUXqrL85KErcNPybBRVN4n8ACmoCpddH9Lqni9gK4ieYObt2HCoDMeYKlEr75m8NzS1wmzhodOqEa/XILuwyquMZj+6BjqtGveMJjNr1KeSzvr9lGcEAIxLiRI8Jr+aPx43LtnZbr61AsCvf710xDzSferxtYfwjZuZ5N6G3vcLUiJXVei0CvGbOV9YuFP/ukN4MvCHzT2WSLmAtbehIwQ9qIrMW20oNJowY2W2YB+SHqvDBw9ejs9+LsXvRvfxyXqGTXEASLYtjTTz1h7FXZKHRs3hm4UTcKK8ATNWZrtU11hwSmBgbCiOSkQNHYVUDAEAJ6vcq3vj9RpUmVphsdqhVimw7tExGBSnF76vnLBkYIwOX84eg8dkFMAAIX/07+h//iiodjPiQ/H67cOw4NPfhLlBT/QqM8G5PjojMkqKCBKEOp6gVinw32yxApLOk6UZgmGzu9rLnK4z47J/bMXPz0zCmH/91H5Gsx/dBrqNmC08hv/9B5gd5D1ABfzyzGSEBRN/yu8evwq7CipFIqEYXQAqTG1ICtfgmRuGuMyu9kaYWiz44kAp7LDhiuQoDIwNcUvokqOCRbOy+R5mknsbeu+vyPrF+c2cLyzC+xFiVOlhBpBFbREhVLN39w4SWF3oJLiVx4F3ruryZfemqubudXKq3aPnTIJ/1etb8kX+VaxPX6GxEYfPNuDmYXHgVEpBTHHwuamy1URvI9rYWUKWrJgtPPYX1SAmVOOR/AEAbwMsvBVbFk3oEh8yuflGAELahXQesazeWQm1WO2obmoTLQM9qRZUNIqUzzcuzxZ537mDmVFNHClrEHz6KAKUCrTZ5Gngoinep5d4AqdS4stHxziqju6XedldIzDr49+E2wn6QGxeMA41zbxQVbp5RbZgAUNhsdrx0b4z7Wc0+3FesL+oRiB/ANBmBa54ZTsOOwQdnEqJ8anRyIwPFSrDFSYi7CupNSMtNqTXkj963AtSA6P/uUP02JCYYLxx5whwKqXLsYZTKfHeAyMx7lUiYlMAHq2pehN65y8JOP3ienhb7pIAnbH8VxJgl8xH6BIAU6nrayqP996qbRcsO0v4AAhGwhlxIdjgQ1aqnGpXDtOW7UH2U5MAOM1eNQFKwdbjmQ1HhNu0SnP1oBiX95EqSj1W5ajQwfHXbOEx7MUf0Gq1I1ClQEZciGjwXL7l2QSVUtEl7RYapbbtWIWIzCy+cziqm9pw//u/iJ6vcfjwmR3WMHIzT7zVhscl1T5vyB99f7NUOuuAAkCbzQ61Clh292W4Mjkct6/ah4LKJqhVCvzxg1/bNaj2BrzVhntW/4yialcCTJEUEYRhSSFICNOgtM6MAJUCpfWtuPOd/UI7nFMpsW72GIx4aYuI2KtVCvxudB+s+Omk54xmP84LrkyOEGL9KCxWO9YfLMXto/oIv+Wbdw53Ue33RhEPValbbXb8ae0hWbETAByraML1S4lpOzW9B8jx1QYrprzhNHS3A9h7qgZT02O7ffm7G72bNfnn+noO6s+6kj9AnvwBQNywHlG19aryFplCxEfnHIkLcZ2rOPNWG6Yv24Wj50wYEhOM+VMGCkToiI9ZqSwhGxClxckq+fmdioZWwRiYEkazpARHb3uq0sgpSuUgVaeW1DTjpNGEVgfBaLXaMX9yKjQBKoF4ydW62OqhHMwWHntOViNOrxHi0WiEXXm9GWMHREKj5kS/c3+DuGKpUJDIMqk62Mzb8N3C8ahoMLudXfN2ho7F41NSMWmQASkGHc7UtsBqs2P+xwdQwLSj6bqwWAE1p4QmgMNZR6yehZmLLDSaPC5fe2AvIFjyR7elQI4ojdmKCZ3LzKswYfrybGxeSOahDpTUicgfpwR+fmYSwoI1Xln1+NH90Kg55Dx/DT7eV4KXvnF2bJ5efwSf/nwGH/5xFL4/asS16QZhf8hM0OPNO4b1qjQQSvwe+/Q3HD3nebxBCqr0fWLNIbcX11Y3lfneBv+e6EfXwOajMqoyD7DxF7Ry63Xcl4oj4iNfPAw9fObWYxXCQelYRRMeZapRAMlK9ZYAsoSstc0qXMUCwJpHrsD8zw6hoqFVdPVOD+xqTgGLzCC/hlN6rNJ4M3MmVyk8KZkzUyoUssSLItUQjLWz3OcAk3mmLaI5ui9mjxHZmmg4JX59djLuWf2z4K/379uGYkisDsccv8Ftq/bg4HNTsW7OWBRVNeHxNYeQ61juFIPO42/BtpVTDcEoaEfcog1Q4pr0WKGtnhwVjJKaZiy+awRuXJ4t+5o3vs/DE9cOFMXD0fUzY+Uej7N17V3gsL8TNdSmc4wHSupcqqJS5Bmd81BXJkcIc4EAaeHXNPMIC/bNqseP7oVGzeH+rH5Yf/CsqAJ/uLReiIn7yzrg4HOThRZ/byF+gPuxGE9INegEgVVmAtnfPb2+b2TvqoS6g58A+tFFkLkiUqmBsGRX/0YA4M3AyR+BQdd3y9J4U9nzdp4NgFOF3sllau/A1JGsVErI6prMQhtVAWBInB7ZT01yWQ/r5oxFQUUj9p+qwd++PubyfgpPviA+LJO0Ujg+NUoQl2gDyG36vKKqJiz67KDohFRgbBJ87eR+TzLPJJ6j219UI2rzmHkb1ueUifz1rl+6G/2YA3hLm02oeKbGhGC9r6paR4v7TI1n9ST9LJrqsWbWaEE8kpmgR3pcCI46vj+bLHKkvBFxeq1ImJNqCMZT1w3CQx8ecLyva9XWmwsc9ncy6NS4ddVe5FWYcPfqn/GvmZkCKXQHtkKrUXM48OxkYZ5waKIeBp0aO/KM/spfDwOnUmLDvHGiC57EMI2QFGIH8NG+M5h91YBeRf4A78ZiDMEBiAoJxLFzJKVo7awxOONwAqCCN3fiqCFxIReND6B/j/Sja6CUbErBMcCt7wHGY8Cvq4EqGRK49n7gLyVdrqj1trLn0zxbF6C9A1O/CC2+nj+uQydKYnGyV6DhdgAHSuowcaDBhdTyVhtmvrXX7YmdJUSdgbRSSHNupa1ATqVEakyI84T0eY4ozszd70nmmZSiCuCVyREuCRr/2OxKctlUhEBOiZFJYYJA5crkCK8VfmwLmCWjapUCCWEaFLmx1DhcWo/9RTVOYlpajy2LJgiP9wnXCuQwPS4Ev52uxa4/T0RuaaMgzOGtNpcYPJYoe3uBw6akUF+/3NJ6TFu2W/S8yGA1qpvEmex2m7gqqdOqseXxiQKhvPyV7X71bw8F3e/oBU+oRonLX/5ROIa8viUfPxyrwCcPXYEDJXUYmRQGo8nS4yuC7HGdA8BLHlerFNj+p6ugUXOiCz1K6ug+9OWjY4QLovCgADw/bQgGx4d2ymKrp8G/N/rRNWDTVlSBQFMF8OE0z6+xtnZLFdCXE19nPNR8BXtgSo/T4Xej++H6jBhUOlR2nZmxKalpFqk4A1QKDEuUb13uL6rxWNXRBnhuAXcUNOdWzogWYE5Ic7NEvwkb3ya1lMl5fqrLDOCGuVn4Kc8oVMfc+dVt/EvpEwAAxXhJREFUe2Icfrf6VxRXN+POd/bhVFWTozrpPVkRnWyUEFI/LFY7HhzfH3/dcFT0/DSDDvlGE9LjQnCmphnp8U6bG/r7UyL6yUNXoLi6GTctz8YzG47g2Q3OthzgGoPHqZQiorxm1mifLnA8paQMTdTjkwevwE3LslFU49zOjpQ3uuxflFDuyDOK1L97TlZj0mBXYZEfFxacSomkiCDMXLkHdgAxIYGoaCTxqodL6zH0xS1gaX5XiI+6E9KkmyOlDfhoXzFuHRWPNl4hzAUDkL84ZvahjXOzXKIVLyYo7HKZRBI0NDRAr9ejvr4eoaHezSb5cQnCygOndgAfz/T+NdGDgNnZ8jN1HTRfFu3EifoelUDgrjXdURsY9vXS9rI7ImO28Bjx0la3JDAjPhQbujjvUq6KB0BQ6FltdhgbzRjTP1J2js3X35N9Dds6ZUFVrXL44IHLva6Ami08pq/IdknFGBKrg1KpFKqR1Ouvtc2KWxwnWwD4et5YwV+Q/W20ASo8c/0gPLfJSSJpm07uJHyq0oRJbzjFGtufmChUAr3drujwPK3CZiaE4s07hovIKWv3kpkQivVz5LcVuRnNrt6u/OgaSLcd2v6M06lQbnI9Tmx/YuJF4YMnRUFFo0j93Bu/py98zV8B9KProOKA/hNJykd5jvxzogcDNptzLrDyhLylipUHVk8m7+NjbvD5ruz5QlTlBBRei1Haed91c8biywNnhQxQd2peWjl68euj+PTnsy7vdcSh2u3KA5+0KsvOHomWLUCJnOemikhgR35PTqXEmlmjsb+oBlG6QJd2JgAX8hfIKdDK2322KimrN8tGoh07ZxLaurzVhj9/cRjXLdmFRL1GNDF7tKwRGYnhAMTV2ZY2KwwhatFcJ53RkqtsS0ca4vUan/cBaRU2Xq8R+SFq1By+WTBeiAb0VLXWqDmsnzNWECZ1x3blR9dAuu2seWQ0yurNeGbdYZSbakXPbU+Z35tgtvDYXVgFq82OhDAN/vKlMz/5Yvqe7uAngH50LVQcIWsVR4D/u4a0eSmiBxO/wOpC4Is/AjUn3Zt4Vxc6SWR5DiGKnMbramBn0hF8gpUH/m+K95GEMvBJjOIBnEqJaZmxeH7TUbTyNmjaaeWu/UVM/vqEBeJMXWu3zEOK298h+Da33IX8AcSKZldBlYvHlq+/J2+1OQUW8aEuc4EAaZO3MdYnS+8aAY2D/Pkyqxav18h66KUZggWCdKrSJMwKnq0Xi3WuTXcS9CuTI0RzfRMHGpD7wlRszj2Ha9MNuO+9X0UtXWnl2F06yca5WbKVVXdEjrfacKrShFn/+xUFxibRhQk7L9UeUmNCzuucrR8dg9xFVlJEEOZfnYJ9RRIlePtNwx4L1gT/1+Iq/P69A24Td968Y9hFX632E0A/uh4qDogfTqxTVmU57791NfDfaU5iFz0YeOA7ecLEt4pvf/EgUHWiwySr21Bb3OlIwq4SoxDSs1ew4TC32VyCzCn2F9WAHY1TqxT4duGEbhvypieYE+UNuGl5tqB27S6wpDq3rAGr7xspzARStFntAhHTBiiRNSASRpPF6+9OTyYmM+9K/mJ02MS0O+XsVujM4O/f+1XwWZPO9VHSducVSQAgOkkDkK0cU0EHm07C+vXRZWdzkan5rbQNTdHRC5PzXo33o8NgL7J4qw0zVmQjt6wBqVEa6DSBOHjWuT8VVTX1OiUs22kJVAGtHpzL6EzuxQ7/3uhH9yF6ECFsAPmr5MSt4crjxEBaDo0V4ttVJ8jfsoNkztAq1XZdIATqPN/2AvQkuf2JiZ2aVyyqahK87Sg2556TfS6tNAHEsHftrNHQqDn0j+4+hRunUiK3tN5jxm2gSoHxqVFuH6eVKXfEloISLoAczBPCtLLPswN4fHIq9v7latyz+mdMemMHZq7c0+7705PJpDd24LZVe0SP/eOWdHyzYLxLxe3ftw3FizcNEYgVFYzkltZj6uKdwudSzzy5KiQ9SXMqpWzlmP3+A2Oc2yL166OQ5iJTs25AXiSUZtB1+MKEXWY/egeKqpqEinVBlRn/vHUoMh37EwA8/nlOu/tITwO7v7gjfymGYGxZNKFHzY13Jy7+b+jHhQON65t3gPyNTCHzfBSeEjX6TyAtX4CoiunrArREZPJ/U3oGCdy9xPNtL9FdJ8lpmfJxRbTS9N79ozAoNhTTV+7FtKW7YLZ4XqfeEjB3r/1ob7HsYynRwXjv/lE45Mgkdfd6SrpYkmZqseDzn0tganFalEhJdWpMiMj7j8Wb2wqEdingSqbklmNXQaVsgkYgp8RtlyW6tFNnrMjGdUt24YWvXC1pKNr7XCmkJJclaJxKiY1zszDQkXoifZwaWFNkJoQKj1NDZxZL7hp+SZwQ/ZDHD0fP4eVbMoTb7AVDbwG7vwSqXB/vHxWEr+eNEyqbh8/UYenWfNQ1yQvFLgb4VcB+nF9Yee8TNdrMQHE20C+LVA+lCuN5B8TtVvb5Xewt6BZNVcBrA5y3nzwJBLuvYHUn2LZeQpgWm+ePRViw5/UgVf/1i9Diu8cmiEgYbXVGBHGYsWIvimqaOyRWkX4WRUp0sFf+h3IqV4NOjYwXtwj3HXlhKnRa16B23mpD3rlGzHwr260tDLVoYYfg5dTatDUmh7WPXIkRfSM8qnPdoSOK9fbU454e9zQDaGqx4Mbl2SiubkZmgh7re7DtR09FZ5X9FxLSEQHAYS4fq8PRc6Ye567gLdgZwMNna/HfvcVIDA3CtGFxSE8IE6Ikb162S9RNyXlucrvH0p4CvwrYj54LXxI1AjRA6mTn7f4TSSu57CCpCNp4QihVHNBqAl7rT2YHA7TAn4vPDwkMjiKkb+cbwIQnLhj5A0jVx9cUi6SIIKTF6ARbj+KaFly3ZBe+WzheyNClczN0Xg7o2EyYO5+5t3430mfPPVrR+mjfadFzNuSU4Xdj+onuY79DRlwIHpuahsVb8l3mENUqBbYsmiAyYZYS3fayf29/Zz+GxIVg07xxovm/zPhQt6/rTNaqVBwjJR30cZqPDDjJnlTMwb5Wp1Vjq8PQuTcSmAsJwUqHRgr2cN88OdBjycofC/Hm1gIAZN+/e3RfjB0Q1WO2CbOFx3dHyrH9uBG/H9MPI/qGe1wudn+5on80rugf7fKckppml1Ga/2afxmPXDOzahe8BuPC/oB9+eAvaUp6zH4ACWHklaQW3mYG3JziFI20tpBJ4vhAcBVz/T4/krzOtU1/gayuZUymx9K4RovuKq5sx5p/bsHrnSRwtrRMIG1s3S44M8nkmjLZltyyaILQffRm2lrZ1AeC9XSdFzzGEENLPrm929udIeSP6R+vw+SOj0TdcPBdIY+jYhA652brMeM9X1cfKG3G0tE5032u3D0OK48SjVpGsvcyEUGxZNAHr54wVTKw7A2mL3NRiwY48I0wtFsxYuQdTF+/E1MU7MWNFtst2SCubk97YITzuaVs6X9tzbwFdH6YWC25YsgtTF+8Uqme+tvZ7CjiVEpMGiS2khieG9Zh5zromMzJe+B6PrTmMTbnncPs7+3Djkh2d3iaTIoIwOEZ8TFq18yROlDdcdNu7vwJ4KULahgW8b8teaKg4h5iEUd4WZxNLGQoukLSBewg8mSAD4vbbhWgbpRh0LjYptS08/vHNCdnnq1VKfDWvY4a+0vgpX78nW9HKLqxCSZ1TLR6oUmBCWpTImFkuESNer8H0Fdk4XSuOaUszBGPR5zk4UtYgRMzJzdatnT0Guwqq8O/vT6DA2IT0OB3K61tR09wmPO/hD3/Fzj9PAgDctGwXCiqdBMBitSM+VI1PH7pStl3dUUhFISNe2oI2G4TIO4pcGT8+dui/PZWndP1Kq1tspN6lEP3GjgWwaTAU7Hxlb8OguFAhozo9LgSD4nwfAeuO7cFs4XH5K9shneY4YWzutNckp1Ji8V2X4bolu5yfx9uFDO/eVs31hIt/7/RDDCsPrJ7iJFCRgwipMh4ht6MHA4/8dP5m6DqC8H7iVjBvcZpPR6QAs3b4vvwdTB3xBrImyI6kBQDCjBUgb+vR3aDxaYVGE+55d68QNSaHmBA1tj0+sdPEpSO+fnL2J5TcJEcGCaR02pJdQoj74dJ6lNWbRVYkJTXNLsbNqYZgvHnHcNy4nFSOzbwN790/ChPSol1mAG97aw+OlDciPVaHLYsmIDkqGCazBcNf2iY8z2hqw03Ld6PVwotIKkVZgwW3rtrrc9QUbeWW15tFkVaAa4udhp+08jb0jdDidA0hvJ0hJLzVhunLs0Xrl55w6bLNWLkHZt63SL3eArodGnRqIR/35+JaYV+Wkj8AePOO3iug4VRKbJw3rsMXpdJkG2+2B28ugvcX1Yg8PCkGGXzvTMhBpVTI3t8Zn9aeiItnz/TDO9QWO8kfAFRLqjyVx4F3rwJm7e65lUDaCq7MA969Gvj8LqIUnr0HiB7o+3L7kjrSAaEJe2LOTNDjjGSOLJdpEXWFIXRHwKmUGBQXih8Wjceol3+UfY5apegS8ieFuwoBnaVqbuWx8LODKK5pwdAEPd64Y5jTzsFB/r51zCyeqjQJ5AQgg+sGnVpEOKlFCksCn7lhsMtBv4/MCajQaBJaxUfPmXC6ugl9wrWoaebx67NXY9rSbFQ0EjVygbHJ4/fOqzC5VCpPVjbh8Nk63DQ0zmU981YbblmRLVRqNZwSOc87U1No+snqXUV4fUu+6LUr7x0JtUPZKzdrmBwVLFSBUw3B6BOulRWJlNQ0i9ZvWoxOMKSWRhG2tFmxMacMt45M7LUECHCmRVjabHjjhxM4We2sHLNzsXLoFxHU6/3kOmOqL0222V9Ug6yUKIHg1TWbsXz7Kdw0LBbp8WE4U9vi1ewka5iuBHB9Rgz+MDa53RlAb5EcFSyajabojB1ST0QPPcP70W0I70f8+Srl23sAAONxoGALkDq14ySwuxW5Kg6oLXEmjVhbgfpSIDbdt/ex8kDhdnHqSHWhvFClzQz8ux+ZMfRBaEJn12jl748f/Cp6PCPeWZG50KkJUSFByHluMpZvL8TqbKfAok+4Bt8uGN9l5I+tpFz+ynaXCoGcChFwEmSWwBVVN+NMbQtUSgXi9RokRwShyEGo7QAOlNSJTjrUIoVWsrQBKjzw31+RHheCvhFBOF1DlK9yJ+7yenHb+KEPDwgnoqEJemx7fCJuXbVXNhqOglbj2O9wuLQeNy7dhcIqstzPrD+CXImiuaSmWdSmN/M27D1VjcRwsp2w4hWWmAyJDcbAWO9nDAuMTbht1V4AEOUYb5ibJdjH0N9F7SDNbJWbQgHgqXW5+Hh/iehE3hvUsaYWC9YfLENLK4/Xt+bB4sY3zqOnJafA1/Mv7exjabLNyKQw4UJhcKwOxx1ii//uPQ0Np4CZ6el6ugh2Z5jeVeBUSmyam4Wbl2cjn7ngCVDJVwZ7K/wE8FKDjfcuyuezu4DY4cDD3mfwCmCJEqchKtkOGCR7hJUHtv9NfJ8+Qf55tcVASCxQsl9MSNkYN29QnE2+E+AUmrAqZQ/gVEqolApZJejfbx4inCR6QmpCWLAG49MMIgJ4ptYMo8nSJQSQrRb1iwgSVQhW/XQS12bECqbRUiRHkng1lsBlJoSKqgbr547Blf/8Ea2ONiR70qFVBY2aw+aF47GroBIP/JcQcqoKDuSU+PShK2TX/5j+kcIJjYL+/3BpPYwmCzY7snLpPCGLQE6JzfPHwWiySOLaQpBX4VQl20FMvGkCCEAql+yspoZT4vXvTuCo4yTKVizsAP45IwPp8XpUN7UKog53kJJL6XIfKWvAifJ6VDe14Z8zMoRW+ZHyRmF7pRcvqQYdpmXE4j/bC4X1wraJb1m+G0fKG5ERF4IN88YJn99TCGFdkxkjXtrmkdx5g41zx3V5tby3QUrUyurNwoXCcYnS1iwZ6GvvIpgapncXNGoO3ywcj+3HjXjkI5IidLS8EQUVjRgcr2/n1b0DfgJ4KcHKA+9MBKryvHv+uRzvos0oydInkmSPmlNOosQ7FLpzfyZE0hcfQE+oLRZXMaMGkfavdLkEgueoiQRoSfs4epA4xo0idqhTGCNFvyzyeloBbEdoIq10uLNB2V1Yjcv6RQLo3gxjXyovVyZHCEIIQFyllIMvg95stahYoo78z/ZC/Gd7IdLjQlyEKYGcEl/Nc+bRbl44HiU1zbDa7Ji6eCcAQjZqmnkcen6q7EmHkpF4vQZ7T1Xj9e9d94VW3obNuedkW5f0hLbnZDXe+CEPR8sboQ1QoqXNKRjhVEokRwWD1goClOJ5PKPJIvzGbHbvrSuzBTKngKuJN53VpDOA8WFa0aB6foUJAw065Dm8DG8aGidbXZVDe1Y1ADDzrb2wWO0IUBLvxsLKJtF3XjNrNG5avhsFRhP+s70QgZwCrbxddCJnW+hHyhux8WAp3t51CvluBCXnG7zVhhuXZbdL/gJVCiy9ewQq6s14XsbcOzMhFCmGi2NOrLNgiZq7Y6AU7/9hFManRl/wCwJOpYRNUjA5Xd3sJ4B+9EJISVN7iEojpM4TWJJFyZEhHcRhyHHWqznpFFi8Owk4d4jcHzcCeKiDub7h/ZzCDwBobSTLEBhCHqs/S6qdAsFz7MRtLSSfOH4EcP/XQHgyUFvkfF/jcTJbKDdLGKAhbV+51rZERMJWuTLjQ/HmncORHBWMdXPG4vCZWsxctU946X1jktDdkFMiezq4atQccp6fij0nqxGn13i0KWEHvdUqBfb85So0mG2yJsqU7NCTQIJeg9J6V6f9o+WN+HYB8dKz8DaU17cgMTxIRGBYRbC0dc6plLInnaGJehh0apesWxbuWpfsuhk7IBKPGgmRsVpt+HbBONE6Yv0C2xhhgFSAwRL+jfPHI+9co9sZQPr8wfF6DI7Xg7faRKQtM0GPtbOcBtbZhVUu81eeKiZv3jkc2QVV+NvXroSmX4QWxQ4RSZsNKKxsQmp0MNY8MhoAcKK8Ab8U14jmHlt5O5Ijg7DmkdGCwe7BklrR+z7xxWHh/z1hwL6kphln69wnPyToA6FSKlFS24LlP57E4rsyXZ7z7u9H4upBhgtOXnoi6DgM6y8oRbxe0yPInzuY3Rw3eiP8BPBSQng/ICwZqCtq96kAgKp84P3rSMXMHUljq2i06mc8Kvnc/oClCSg/5CR/ABGjeFNhdIc2Zh6rsZQQUcBJROOGE5JZzlQAKcoOAu9OFpM/ALC1AavGEoIo972l5tSArIikpMZZdcota8DUxTsxNEGPTx66Ak+vJ4rrmBA1Ni/IOi8O83K5se2daDVqDpMGx7T73uygt8VqF0QkLNFkCejAGB2+nD0GRpMFEUGc23Zbeb0ZE9KIUevT63Ldkld6UnFX3WRnMAHgl9O1bsmfSgFQcaGn9bTlWIWQJ2qxkfXLVgXcVTr+NXOox+VMT9AjPcG76gKnUmL93CwXoQZdXun81ZXJEbLvw/42mgDxMqmVwMZ549AnXIuRL28T2ckUVDYh71wD/rTmEAqq5H3uiqqbUVzdDJVSIVK+y2FgTMgFH7Bnf7d4vQZ/GN0XH+w9hdIGYvFTWu9UdOeW1mPKG7tFr89MCPWTPy/w/VH5jHIA0Ab0rHXXVxIhuWjNIaTGhPg0V9tT0buX3g/foOKAOz7w7TVlDpLmDtSSBSDEywUKoPYU8M4E4L1rxQ+FJTvTPHxFdaH7VjYlhuU5wIxVJDLukZ8ky90fqPJQDW3ve7OozBOLSCrzyLxWnNhH7XBpPW5ctlsY/K9otGDb8SqYLTzqmsxYujUfh8/UgbfaYLbw2JFnbDeb1xNYs142+zUzoWsFJlcmRwjmxixYA9yiqiaBDOVVmHDrW3uRFBGEsGANDj43GfF6MQnWcAr88YNfMXPlHhRUNLab09ueaTEV4ExdvBNv/JDv9iRjtQPJVJDjZgbJbOGx6PMc0X2VJrHVCyWd7/9hlOj+xz7PIWbLTJYxXcaOGCtTX0W5Ci2nUmLdo2Pw3v2jPLZ/2YsDc5v48y02wNjYCo2aw4FnJyMhTLyP3/rWXlnyp2YWZcbKbGKM7IH8DYjSYsldwz191fMC1mx855+vxiNXp2DbnyZhYAwh1Wkx7i+abh2RgPVzLm3RhzcoqWl2SeFhcbKquUcZZ/cJ14JjDm92ANOW7RblkfdW+CuAlxpi0smc27nD4vuVasBmcX1+gJbM8dHINSmoJQudATyX66zEARBV3Wxt4tdygSTNw121raOgFcD4Ec45Qyvv9A40DAZueZuQUneIH0HIrTeoL3W9HT0Yd12ZhL9ucFZDDSFqoY0GOFuNL2w6Kszavbm1AIMMQThd2+qTd5YU0hb0a7cPg81GPsOL+G+foFFz+PmZSbj8le1os9qFWislULzVhsclhCnPaEJ2YRWyUqKgCeAQrHams/cJC8QZh3fe4dJ6LPzMOafpC3nlrTYUGk1Y8NlBkZ3DkbIGfLdwPMrrW/D693mik5E2QImv5mfBaLK4nZXcX1TjYkB7y7B4l+dxKiXGp0YLqtlUQzAKHIrCXIcfZGpMiM/teW+/O/uetJIqNwfKVr2kAhdtgBL3v/+LsFzbHp+AaxfvwOla0iZ1E6uM+PAgFFeTk3hLW/snSasNPcZoVzqHq1Fz2LxgvDC+cPuqvbJk9ukb0vzkzwtIleQUdFb2QjkgyIG32nDrqr2y23lPGFnoLPwE8FKDigNueYvMwbGQI3+AeGbOHUlTcYQs1RaT5z15EtjxGjDoRuCjWwGbqxEuVIHOClxZB1rBkSnEtLryuPj+u9cC/SeQGUDW1JklqpTYxQ4Tt6Qp9H3IfKAnQsrO/PWfQNTOvBngNOD7jROSAVgYG8XrmB5TzBL32BNG59WvN7NbcmArbrllDSKxwJF2kh46grBgDXJfuAb7i2owMilMRKBOVZpc1oU2QIX73/8FmfGhaLXaUFDpnB07U9eKlKggFFY1I82gE9kwvHbbUK8Nk6naVIqhiXqkGHRIMejwxg9iv7z+UcHQqDn0j3av3mRbqwFKBfY+fZVbtSebz2zhbaLfgUKuPU8NqzuqjJV7T5qAIk3wYFvoBp0av5yuhSFEg/L6Fjz04QHRe/SP1mHV70fh+qW7XT4zTMuhroVUrIurmwVBChXJBKoUaGXMe9UqBSxWOwI5pXBhdLi0HnnnGr1ug58vsKSQtt0XfXYQR8obERHE4ZuFWYgK6VrS0hvscjoCTqXEm3cME4RbALD6vpEYlxLlkyn6+YCcaTxFTyKqHUXPWMt+9AyomJOYKlD8mKeWKBWCLB9JZuFO7wNO/QR8eKM8+QOIb1+0w2vPl2pbmxko2EZax4/8REggRdQgQsYCNIRMSgmcinPer+KAh7cD96x1/Yz6M+R7uGtNs9/3/6aQaLqnTgP3rgOeOo2iOqvHdhcApES5P3AMMgRBG0AqYp5mt+RA0xgeY6pm5wtU7afTqkXt2KSIIJfWGa0y5ZY1uJitAoDF0Vqx2WxIZ7J3n1x7SLbtIm2ZF1U1uZA/BYDN88dh3aNjwVttWPvrGRe7k6MOW5P2vufB56bggwcuR+7frmn3xE/JQ4pBJ+QIZyaEIjkqGLzVBqvNLtxPo+rYTN+OtJnYzOLMhFDE6zW4mRk/kLbSqUr97tU/44H//oqnvjyM/zBm0rTySu1kMhzvTVvpCgB1LbygfM5M0GPjvCx8t3A8Eh2Zyyz56xcZhC9mkxb5gWcnI50Zl7hx2W6YWtxckPYACCpvJfnuCeHBiNJ1Pfmb7shmvvY/O7F650n8b0+xaL3kVdThhsU7kFdR16WffT6QHBWMoUwe+FUDDY4Lr56RM0xBq+MAwA65pEQ7xU29Gf4K4KUIpZuf3eo4uIT3Bx75EWg8B6yfTUQUnkgaKwQpzwHW3Ovdcqg0wJz93tvBiPwFA4EnTxES+PYEMg9YdQJ47zrvlcUqDkieAEQMEGcJA0Qt7c4Qmv2+ZQfBV59CiSIe8X0noqzOjObWNtfXMHj39yMxPjXKGSkWp8NL0zOws6AKkwbFYEh8KHirzWeTU7k0Bjkk6AMRrQvw6j0FdCIqj1MpsfSuEaLqF/WsY61mKAI5JUocLcbCqmb0jXDOnbE5tp7MpOVgB1BlakVfi6uggaJfhNZlHlEO3niQ1TWZ8dG+M/jd6D4IC9YIog020o6aXacadNg8fxyUCmB3YVXXJMIo6ClLgfwKE/KZKqtcokFRVZPQlpMS4zfvGCZa3oz4UGxZNAF9wrXYmFOGp9blAmAGPux28FYbHv3oAIqqxYQ6kFOguLoZN6/IRmZ8KNbPzcLdzLiEHcCmQ+W4Z3Rf37/zeUJJTbOwrnK7oRV4orweRx2/wcnKJiGX+7lNR/HTk+PRyttw7WLixXjt4mx8vygLA2PCXN5HLs2lJ6A94VZPASsgyytvwLzPcgAAhZXEfL4ruygXAn4CeCkiMkWe9FDUniLq31m7CZlq78TPZvP6gnM5hIx6SyhYI2a+lRC/uz4Ri0F8URa3mUmUXM1JUv20ell1YL6vPW4EbvusHDllBUxb0PPL+znajBvmjSMzap8exMxV+zA0QY95k1KFtpyvbV+5NAY5lNa3YsRL25D7AokRa/cgzFr9dHBeM8WgE+Z+MhP0+PShK7DpUDme2XDE5blL7xqOWR/9Jtw+XdMi8reTRo/1ixSbSe89VY0x/SNF+bcAIZYjk8Jw07JsWfLHKYDimhbc8fa+Ts+h1TWZhWzg17fkI+e5yQIJpEShoKJRIBEFRhNmvpWNVsmwkTZA6RUhlUJKUG5btUf0+Jt3tN9KT4sORr7D6y85KliWIGrUHKYPj8ffvjommh3MLWvAjct2i2ZeKdjvmFvWgK3HKnCuVkwShyb2rBawFFJroa5uBR4+676DcNVru5AcKe7QLPw4B8vuHQnASfSkaTqUbPcUstWdnqddDTlT9/1F1egTrhWSi3o6mZVD71lSP7oOKg6YtZNU0QDS7o1IEz/HeJwQKbZt6gm3rALu+Ni7zw/rR/7GDfe+9QsQ7z2OOfBRAhs33HlfnJftZCtPMo+NDs8zq4VUPoX3GS42hG41AQf+R/7SecJ5B1B0ywbklJG2Gj0BsjPvKQYdkvTOZc6IDxUixmiFhM64uVO4egu27dce7AC+OlzebqvR1GLB5h17RBVPr9XRcKpbAWC9Q125dtZo3P3ufhH5o63EgTEhyBoQKbQYAXE7cdGUVOFgK5hJSypMkcGBuHVltoj89YvQ4sCzk2E0WYSYOJdlldi/dAYf7i3xeBsArDYx2ZOSP4AIKM7UupKo9sCqvvtGaGGxit/76XVHXH7v5KhgYfvJiA/BkrtHYMuiCVj3qHsyzFttKKs345dnJuHd349EisEZnydH/qRQAZj98W9YvsNpx5QeF4JBcd5txxcKrFrY0/rpKG4eFufx8aJq8WhNvZnH1MU7MXXxTsxYkS3sI6zQglbP/fANRVVNLuQPAP664ShGvLQFphYLZjiOo1J1f0+HvwJ4qSJQBzxV4jQ1VnKk7fnlg+Svt3N5bHUodhghd3XFMk9UAXBUCOjjFceJcELl5VVggIa0fd+eQMgfVfk+tM33dJHaYkJyKQxDgId/dJIb9n1aTcA/EwHYga/mA0+fBQJ14MP7w1rV5DZBoV+EFgrYUcJ4hy2+czh4qw27CirxyjfHRe2xfpFBHar2sJC61ruDAsDQxDDBk1Cu1UgjsZSwIlHdH8OUp7zeLswWHntOVuPNLfk4UtYgiA76R+tkRSFrZ43BE2sOIa+iEfes/hlfzB4jEJ/kqGDUNZtx07JdaLORSl72UxOFimsgpxRV9M7Vm4VEDYrl91wGnVYNjZoTqonu0BWB7xPTokVGtxMdKtyOYO4nv2HT3CzfKw2ObaG8zpWIsa10CtZX8LHPDuL6pbuRHh+KjXOJYIwSxNyyBmQmhKJPuFaowvYN10AdwKGQMYIW3lcJyBRcAQhHBAGPTUnBvKtTe0UVpTsrWDqtGkdemIpNh8qRagjGz4XVeM0RrSeH8gbGn9Dx20rVtlITcj/aB2+14YwH0tzSZsOGQ2Wiajur7u/pVUE/AewgesOP2y6kpsaxGcDsbN9mvdh5ODlFrQAZ411bK7BqPDDvF+9bioE6EisnXUa5WT1PYNvWhsGE/AVo5N/nyHo4p5vswJH14Iff67RZSdBj45yxmLlyj/AtAzklVtw7EtOWOdWSA2N0iAsNdJtCUVzdjJuW7cbSu0d4TN5wh/b8tVgMjtVhQHSw2zYWb7Vh2tLdsAOwQoWZlhfxn6l63HRVVru/FZsMQnG4tB5fHjiL6cPjkRSmxrUxjdhaEQQrVMhM0CMwQCWQssOl9diYU4bpw+OhUXOoazIL5tIAiVO7Zfle4f1beZswU5iZoAdvdV235xrMyHD8/807h2POxwdEFUIWS+4a7rXS2N0xID1BjyGxOhw7Z8KQWB0GxobgVKVJeC5vteFPa3KE50cFcahqlhcd5VeYMH15NjbOyxKyg9uzS2GTSCwy5MtdtB+nUqK1jRe2o6NlDThaWocQrRpJEUGiGUa2CkttYaSf8frtw7Dos99wvMKVGCoBxOsDcZa5QBocG9p7j6ddDJ1WLcxBXt4/CneO6YOVPxaBUwDvZBcLz1MCGBwfKswMUqJHFeg9cQawN4AdM5Eq2Fm8t8t1lKo7rJ26A34C2AF09sft0eSRtny9RUfn/yhqT/luAePrMlK0mcUxbg96Md8IABkzSOUPdgAKIGOG6OSXW1qPOR/9IqK4rbwNdrszqqtfRBC+nD0GB0rq3KZQACRd4fqlu5GZoMd6H7erpIggpMeFeEUCj50zoaze7HYQu6SmWZR6YIUKCQMycKqmfZsGNhmEgnoevvRVLg4nvoa36w/C3GcYzszciGSDQw3oIKP0uX/76hj2/uUqXMPYRVCcrTdjYIyOWJok6rHmERKBFq/X4MZlrlYrieFBMFt43LRsFwoq3V/RZ8SHenX13t4xgFMpsWm+0ztOStykKuWGVvH66h+phUqpEJY1z2jC3lPVInGI1MqHzWNOigjyWOl84hr3nnXS+bNHP/oNZQ2toiouQLa35Iggl5Y6rci2tlnBW62y5A8gQZF/vTEd8z/9jVR2VZ2rlF7siAoJwvM3p6OuySwigHuenogoXZAs0aNG4X74DvYY32q1493fj8S/vz8hijsEgFPVzouffhFBiAsNRHZXCbm6GT2MffQOyHlsScE6+9P/my08CioaMX35bkx6Ywduccxq9GrQebjZewCFlESpZF8iuu7wdmavs6AK4o9nOpTEZu/nGzkNMc8GgJhMoP4s4kM4aBh7eBoVxeKxz3JoGjKKa5px97v7MSwxVGQnAACpMpYwuR2YQ+NUSvz7tmGi+/q7sZtJi9HB4ujLsaKEgopGITkkLTpY9Jq/fJmLSW/swLQluzwmlFCfPADQcEq8ND1dqJ9G8+VQlpOLBU3lISSrKoXvuW7OWLw6M1N4bkubFdcs3gWjyXXdDk3UY+PcLGx/YqJA/pIignCmtgWFEoKnAJnjuXGpZ/L3j1vSscHR7mxvNtKbYwBtEZbVm12eK31P6YzeqeoW3DGyD+JCndZMr3+fJ0qXeXyN0xKHVl3vf/8XjHhpK3irDRvnZQkJFinRQUJaizZAhTH9I92uhxsyxfF/ZQ1OU26pdcxX87MQyDlPI0nhGqEdX1DZhLkf/wZ3UKsUmP3xbxgYE4L3/zAKh1641mfD80sRYcEa5Dw3GX+amoac5yYjVq/zmAjjR8fAWsAMjAlBYrjWhfyxIH6Wzbj8le24//1fhOO8WqVARFDP3K575lL1cLSnAGOrA6nRwYBCgQKjSTA+pThS1oBCo6nHDzy3CxUHxKYT8+T/Xue8/561wGd3uiaAqDjgwW2EWHk7s9dZsArithZyW5rp6w61xc72dsVhkl4SPQxt/J/gnuQSCxMWuWUNOHimXpR7m2YIxqZ541Bc3Yz5n/4mHGA6EtfGW21YxLQVAYDnxZWlVfdehjd+yEN+hQnXLdmFlOgg/PnawViyLV+oHKZFB2PT/HHYNH8cbl6+G/nGJpEhc57RhOkrsrF5wXjZkw31yaPVKAB45ZsTaGmzopKLgy1uBJTlYgU1zQfOSNALFSQFAKNJrMx+/dahuKxfuFCZS4oIErXibTLtXzuAR90QkQCVAm1WO7QBStx2WaJgXN3e1btBpxYSTxSO2+4gd7yg1RpPePk7cdTh0fJGrL5vpGDOzM4bsVVX1jycJlhQ5bSctRBbOdSoOVTKEG5A3vhWp1Xj0PNTsfdUNeL0WtjtdpFJ9OlaM5LCNYKtD0W8PhBljgrzkfJG9I0M9pM/HxAWrMG8yakXejEuanAqJdbMGo2bl+9GXkUjnlx7COlMq53FSzen47lNxMaI7of0OG+x2nHlP3/Eoeen9rhtvGctzQWEL23Z9jyM2OoAm3AgvcoHgPL6lt5PACksktbj5oXA/V8RSxkWVjNw5gAw6v7zQ/4AR9vXEREXoCW35SDndyfT5tZUHsJkQxN+MJLfjpIBFhnxobDb7QKxSo/T4eXN4uQSNacCp1JiUFwovl04oVPzOiU1zS6myiV1rcJ8XEZ8KGqaLKJtsrCyGY98dED0mvzKJly/ZBe+XTge3yycIHjt3bQsW2j35VWYsPVYBaYMiXFLAlkbG5YQKlXXgq8+hexqHXI+OCi832X/2CrsIwlhGpTWuc6Vvb3zJL69jET4nao0wWqzi1rxvsAQosa6OaNx0tgiIkXeWHwcKKljp0JxoKTOrW2P3PEiOSoYGXEhskklnpAYLh7sX/TZQWyYN06UTsKah9MqpJTkUbDzmu48FFMNwVh570i326RGzWF8ajSKqppgtQFD4kJwjPle5ZLfMdWgw/pHx+Ce1T93m42KH350FOy+cqa2BfmOi/Ij5Y2yFl9JYYH47BdXlT+LVt7WoUSn7obC7kUwaENDA/R6Perr6xEaepGQFQa81YZbVmTjSFkD4vWB+GbBOIQF+67GNFt47D1VjcjgQNy+ao/boVEKbYASB5/reVcFHUabGXi1L1H2UigD3aeBxA0nCt7zRQKlM4BSePK7s/JEabx+FjG7jh8B073fYMNhIwwhGtjsdsxmqkz/nJGBGSMScPvb+wSjX7OlTci4ZbH9iYldMh9ChBu7XKKLvls4Hna7HTNW7oHZUVnzRiucZgjGNwsJ2aJVNulr0+NCsHHeOADw+gKK3d88LUtCmBalMgrWLYsm4Ik1h4ScYygUgjlxobERZoedSiCncLFWGRClhc1mQ1GN83egHn3SZfT0fcwWHsNe/AGtVjsCVQoceuEan/fjgopGURwWAJcuAYtUQzC+XTgBhUaTyFR7y6IJSI0J8Ynk0cd35Blx//u/CM/94IHLkZUSJUQZ0gq19Lux6weA8HsCQN8wDU7LkHcK6kcHeL/N+OHH+YB0X1k7azRuXJ7d6fftaKZ7R+ALX7tImEfnUGg0CQevMsYkl833NFt4bD1uxC9F1Zg/eYBL/JOc8tEdBseF4MlrBmLsgMiLh/wBDpuWk8DK0SRODXBP/gBCpHwVgPiCVhNR8GbMIOphqeqZkjqAtKKrC8V+d9WF4CPTnCcpwyBCWGuLYdYl4vKXfxJsSFhbEm2ACjNGJKCs3iwy+pVDV1Y/OJUSG+dm4ebl2UK7Nj0uBFabHduOVwiJG3YACyYNwNLtbozAHcg3NqGoqgkqpUKoskmpydHyRhRVNQmEzBtRVEFFo7C/se/Hkp/MBD3+98dRuPyV7WhjCBGdaWNzjrcsmgCVUgGrzS4iVEvvGgGlQoE2qw1zPiG/68kqV0L50b4zLu00byw+lEoFYLVDoYDQivWFyLC2KlRlmGbQ4V+3DkVpXQvMFh7/+i5PsPhobbMh71wjlNIhUgfcpZO4aw8DkK0cShNLpN+JHXEZaNDhjTuGiXzSTteZRW186TbD2pT44UdPgnRfqTS1IkGvQWm9+ILG24toAIjXa/DNgqweea7veUt0AVBeLz4p2AFszj2HO69IAkDI3dAXv4fFwe0+2FeCX5+9WkQC5ZSP7rD0rhEXrzIrUAfM+9Vhsnzc83N9NYL2BW68+wRYeWD1FJIcAgAxQ12SQOxfPozb2v6BnDITQ2yIcGR/nlFkQ3L7qv345ZlJOFBSJ1RgqDGznEfggKggrPr9qC63ZtCoOXyzcLyjHWfHE2tyRFY0ALkavTY9rl0CCACnq5swLiVKaIlKq2qpDuNfXxRvp6tdBRMDY0Lw5ezRAtmh6+XgX6cIiRJ9I7R44/Zh6BOuFbVo2eQDur7VSghpIunxoSL/Op4XK1N/N7pPu+uBgla+TlWa0OJw/DbzZO7NW0cANsKOknLaLThS3ojAABX6R+uw6PMckb9bSW0Lpi3bjfRYHTLiQ3GkrAGZCXrBWNwd3LWHAbK9/PLMJGzOPYdpmbHCSYpTKWHQqfHlgbOYlhkruhhmR1zyjCbM+8R1vjL7qYnYdrxKiIhjQfOOaZWxpyVU+HHpQrqvjOkfic0LsjDipW3CvO+Ku4djxY+FOHrOda5fDmX1Ztyxah++eWxCj9vG/QQQwJj+kSKfHwWAaZmxAOAIjT8rkD+KlT8W4fmb04Xb7IbjCdoAJfqEaz0+p9cjQENi5PK+k88FvuFN0obtLgFImxnY8iJE3n073wQmPeP8vNpiJ/kDiLhDAkXFYTS3HgOQ5EJsrkyOEBkQF1U3w2iyiCownEqJN+8c7tLmA4Cld1/WbRcBVBF4qtLkYgnz+JRUPDKhP3lOdLBoHjBABbRZAQ2nENqosz78GVNimrHm0VtQ1sjDoFPjlhXZKKxqRkp0EL6aNw6cSulTLJZCcu28cHIKHp04ABo1h1StWEyhUXPQaQIAtOB0TQtuXJ6NzAQ91s5yKn9FB1VH/i3rfXe0rAFfzxsLY6MFcXoNkqOCkV9hwk95Rtw3JsnrcQ+28qXhXA/k3pBfkbeYTIu6X4QWj32eIztoLnyfcyah6ulN+1QqymErEbzVhrvf3Y/csgZ8tO+0QMRMLRZkvrgFdgB/WZcr6ogkRQSJREHFNS0u7fqcMw0ID1IjKVyLEoehd3xoIN763UikJ+hJrJzjO+aWNbhY2vjhHqYWi0DYWWLeo+3Fegnk9hWNmkPuC1Px1eFypMfrYWx0Gs1brHb0iwxCgErhUSGcX9nUIwWf/q0E5Ec/9MI1ePt3l+Eft6QLBzt6sKbqHhZzrk52eY+Dz03Bu78ficW3D4PKTZumo9FOvQ4qDhh4HRBDbUkcKyRAC4y4lxgudxf5e7Uv8Os74vuz3wDemUQqfwCpPLIRcm4wNpKU/imxMVt47MgzAgAOPDsZyZFBoselSI4KFsWaUfxlXW63WwBRX0AW3x89J2QNL75zuOixNivw4s1D8MXsMQAAFaxYp34Bb9fPAv7vGvSP0ECnVeO7RROx/YmJ+O6xidCoOZ9isXirDUu3FYjuW7KtEHe8vc+t3Yo0him3tB5l9Wb0j9a5eBe6E4I89ulB/PGDX3H90t24/e19GBIfigVT0nya9WUrX2Ym2iLVQAifN+RX5C0mIX+ckpApT+QPIB6Bvl5E0vawtA0lR8QAEhPIilw2554TvU7NHOAyE/TYPH+sYAejCVBizkcH8MhHB1BS2wJOQfb+soZW3LJyj0cLIT88o67JjMwXt+CpdbnIfJHEkAHOCwtvbJr88Ay5fUWj5vDJ/hLcvCJbUOFTFFc3Y9ndl6FfpOd9f8bK7B73u/gJoAMaNYdrM+Jw1+VJMJosMLVY8OWBs8LBmsVPT453mQEESOVlybYCLFp7CJ6qwgs+OdD7/f+8gYoDHtkOzDtAWrD3rgP+XCwvwOgqFGeLRSgsKg7J59iqmHzh8GRA6biq5jT46/xZArHhrTaR1xqnUmLL4xM9Eh9OpcSGuVn4dsE4/GO6s2LcEZ8/X8GplPjPXSNE99GZPd5qw9My7bkXNh3DX9YdIUkRCiOJfwNRPdN1R+fjpMbH0vvkUFLT7BLTBrj30mMzbSncWeTIPRcgHniF1c6Lro6ue9YXjMWyu0d4nQnLvoeWkRT2Dde4jUuT4sah8bjj7X2yPoWs/6g3kD6vuKoJZguPj/YWC/exHREALibWb94xDGHBGhx6fire/8MoxIQEiiqwvF1Ui8dXh8vJ/KNjPXjTxvaD/FbXLt4hS8ylbfnpF4PHbA8Cm6wjRUZ8KPpFBmHV70YiMcy9HVRLG1EC9yRc8i1gWjaP12twprYFiz7PEakT6d/0+FDce0USbh4WJyq7s5CrVsjheEXTpdPyYFM7vPXd6wz6ZRF/QTkSGDPMOXNYW0xEKABgZYQqtUWwxwxF5cC7EZE2BhynRv9QM5DzMbLVWbLD9LTlx1ttwuydSqlAXGigMBM4OF6P1JgQrPn1bLutUndqzo6AFRpQLPzkAJbcM9LtAe1IWQPe/t1l6BueDvOGDwn58zYbuh1I5yLp2IQ7P82Smma8/4fL8Mn+sxifGg2dhnOZm2RbX2/eMUzUcl9930gkhGlF3nSBnKJDmcu00llU1SQcJzIT9EgxtE98pe/BHnMAIC40EJe/sp2IilQK9IkIQmGlfEvpqoHRWPojmd9k287tpZPItQhtkgvVh/93QEhYofh6/jjhmMdbbXh8jTPyMTMhVCBvGjWHvpHBLhF7KqKVEaAL5FBU1eS+je+HLA6erkWFSVxBosRcmvySV2HqsekTvRGe5rnnXDVASPrJiA8F7ApRvCGLkUlh3bykvuGSJoC81SYMIms4paitw15lvTozE7eOTPTq6p4OZ7cHq/TI60fXIEADPHUaOLUTCIkBNs0Dzh0GogcCD/3g0duPQlFxGIaKw8BOwB6TCUXFEQB2TIACEQHvo6ZN7TJMz1ttmLFyj6gFSS8eVACyn56IWL3Oo38k4NmyoyPgVEqsnT0GU9/8SbCgOVbRhBaLVZjbS4kKwvWZcVj2o1MUMuuj38hw/qNbgfoS77OhvVie9XOzhFZjn3CtLAlgyQzFm1sLcOSFqS7Po+udzgay84hXOWYyqRciQFqvtIXckeVPjQnBBg8qWW/eg342exEoNc++eUU28itMwqwpJVLPbzomnIwoceatNo/xUyLlbowOGx02LHM+FrezAEIe0gzByDc2YWiiHgNjnctYVNUk2sbfvGO4IMChpJau/74RWjx13SColApBkAMA8z/LAYAOxR1equCtNsz66FfRffde2Ucg5pxKiY3zsjB9RbYQj+hXWXcd2ONWTkkdnvzSOTP+yrfHcdZhdH6krAHP3TAIL31zQvZ9jCaL2wLShcAlTQAPn6kTGL3ZTf9FG6DC9OHxXh2kaLvvRHkDHv7wV5GCTwqVOy8HPzqPAA0w8BqgqpCQPwCozAPqzzqrkTTCLn8L8Pldbt9KUeFskypgx76b6rFXf4NLdU5u/oxSfCuA0f/cIfjNeSIeniw7OoqyerOL/+Dhs3V49dZMLPjsIAqMTfj2SLmLoi23rAFFNa1QKWORBKXPBwt3lUxpPqnc+mBbWizWHyzD78f2E26fKG8Q1ntuaT3O1LbIkuxNc7MwfXk28oxdc3L0xibGV7A2LqcqTQxhJccm+tMcKWvAtwvGITBAJXwPSu60AUq0tNmQER8Kq80O3moDp1KKW4QVJty8fDfsdrhU64Tvp1Rgy6IJomqru+qftPK4ZpYzl5lWRliRHUVuac/NSO1pKKlpRk2zuPq3aIrYPkuj5kTJL35i3bWgBu4f7i0S3X+21oy+kUE4Xd2MzAQ9Ptx3Wvb1mQmhPY6UX7JbSF2TGTNX7fX4nAS9Br88M8mnCgynUiIjMQx/n54hup9d0RnxoZfkzIuv80mdBq3yAfItTBUH3i5eFltwPIqRKNy2xwwFFbDYocDZhOuQlRLlsk24mz9j8fevjrc7BMzm6EqrjB1FUkQQ0mPFJ9lPfzmD65fuFpRrhZXNLnYGGfGhePzzHI+ZuO4gzab1dfjZ3bxdWJAzes9s4XGrzD4snUekRPTLR8d4Pat3oRGv1wjbgRzY78iSO2pNU1hpwtTFOzHD8bslRQQJHooA8XgscNNiBoBj50w4I5mTlF7k0OqfNBe50GhyyUCWM8VPi9H1uBNiTwW7P0QGB2Df0xPdzqF7M4vrh++gFzr/23fG5bHT1c1INQTjtduGii6qOCWwcc4YbFk0Aevn9Dyro0u2Avj+HnmWzqK03tzhku341ChhvimQU2L/01cLGZtd7f3WG8BWCTIT9HjttqFQKRXduy5olU8a7cYYQJfbI8G6wPHKAPTDaVj0/aC8/b/g4jIB3gw+90vctTsevy47IDtfxamUWO+YDyOVFytmrtwjGoZfl1OGb49WeGzrsjYEwxJDu2QWkFMpsXH+eOSda8Ths3XITAjFjcv3eHzNu78fiX5RwcI8nTcWJyw6W8mks3I/5RlFqrul2wpxfWYCOJUS+4tqhOoYAPSLCHK5sJJrqfeGfe9MbYtbS6lUQ7Doe7LRdRRmBxFk84I3MlVQKVhLI4o/fvCraFuP12uE+UDqv0g/Pz0uRLAcuml5NnJfmIp4vUYYrQlUKZBi0AnPSTUEY5Pf+89rtBc/6kf3w11XgqLA2ISztc1CVGNimBZfzx/boVSx84VLditKM7gXYCSFEVVoZ1pF9ET+wQOX49DzUxEWrEFqTIiQFsBbbSioaERBReMlodZid57c0npct2QXqVB0t1qNilBY8rd6MrDySmDllUj49o84jfj/b+/M45so8P7/STJNkyZN6JXSFAqVHkhbDgE5yqEcq/ugILri6rqr7rqLcojI7m+f1dV9dl33eFa8ONTVZ1131wuVQ2XVRXA5CrLK2aL0gJYCLU0v0qZJms4kvz8mM51JJm1aeqTt9/168SpJJskkM5n5zPf4fAEArWCgbeYvDLT2CjA7HuCfE21E5ajb8NUl3nIhVMeqkNocm2JC7og4nPyfG/DLb4+VLeNq43DobH2Hq6zTMpiWHo8Zf/g37nntS0z4TbvdQ3dhNGrkpJpx57RRGJtiVuxE1TF8pDMv1YTrx1qQnmgQl+vqb6HDSCbH8ul5ruOoIKNRY1ZGIkYNaz+AltY6xe9e+h5ajQrbV84IOjEqCdGu0B+/U5bzYu3b8tpUrf9jZVoMoveigCAOXrt3Soevq9MyeP/BGUgwRMnuf2RBJo48Nh+j4oJPVMK+znJe3P7SIRTXODA6Xo83f3StuA6MRo3vTRslPkfoTj3f6JIZXa9fNhG71s7BrrVz8PGaORE5GSGSoehe/xIqKyHl/r8dgdfrRZbFgAuXXfjBX76K6PP7kP0Fzr/aAq0GQQbP44YbsHXFrB7pTgs1mimwYWAoOOFbjNoOx0L1WR2QtPsXgNpxEaMAeGJHIbo5ICpce1ocVSeNsoQrhhiNGjtOVgfdv/7TYszOTOpwex86Wy+bNHLThgP4bN11PbKPSKMJ8TEM/llYg/EjTMiwxIq1W0KkQdqxKvwN57cR0ny4o3nLAbCcF8te/gLnLrvFfSea4SdUCO/x5aPzcPPGApTXO3HHnw9jx0r5yKWOpmB0Bst5sWTjATFq1VdNC4E2K698fzJmZyZ2+r2nmPVB5tLSLl2W8+K2lw6hvqVNfFwF4If5o2HUa/Hp2rli44lQSyjs61K/wIoGfiLJpw+3i7jFE1Lw2PYi0TlhUd7woBroY5WNWDoplYQfMSCRHjctRi2+PNeIP+w8heJaeR2t1Hy/q5mTvmbI/hJ1WgYnf3UDDp2thyVWB7WqvcizNwq8pQTW0vS5COoHjlReVpyd2OeFsXGj4Y7Pga5Bbu6tbT4HJF0N1ErG10lG1XUnBRPKmLiournT7Z1ilhv9VjS4enQfEVJ6genRtPiYoBFdafExkukVfKownLFnOi2D/IxE+XfWWCGft6wwC1roKOW8vqAZxK2sF0tfPIQPV+Wjyu4G5/Wh3D9arrjGgSUbC7BzzWwAEN831BSMziizySepSNOpfcnoRAN0WkZx2wvWQ4+8czzIouI3i8fh1kmp4vdQ2eCU2bskx0bj04fbLV50Wgb/9DcRCEJfEP6BrgXnGlxYvPEA/rmGH29l1GtR+KuFsgkV6VpGTIepAPxiWxF+89E3V9zZThD9hVQbTB0Vh5Ja5SYqoekp0ruxh/SvUKdlcP3Y5D5/X6FhQIwARmB3UE8jj8So8e7yGYiO0vR9PaSGATP9fuCfa+X3J0/gbWIaKwAvB6g1QaPqunphII0a5qWaAZ9PnEcr7dBUIsNilNVV9dQ+IvWCU0qPWofpZZMhymwOaBm1ZHoFn84Id+yZICazLAZ8sGoWdFL7HYXGnMBaUSXvrVKbAzdt2I+yWifyrCaZxUuxzYEymwNr3jrGd/z6hWp3OqkDZ4QDwCPvHO/1aL1gkixY24RqGFOyypHyxAdf4/cfF8PVxiHPasKfbp8gvm52cix2rJwZJMSk+3hafIyYqci1mnhD7dr20ocSm9zP1KjXivPThdd6d/l0vLq/HE/vKgHQXgLRH8fdrkKj1YiO+OBEtWJQA+BLHl6/byryMxIjet9R+Xy+Tg3pmpqaYDabYbfbYTJF1iy7gYpw5Q4MnaaQnjQ4viJaHfD9fgRU8PEpq/s+BUZM6ZXRdNKTCAC5ibDVhHcfmBEytdfT+0igXceb918rmg8LEcDzjS6ZkbIg3AR/MYH0hBh8vGZ2h9uxtKZZ/lpJBn4gOrzBjTl+ztY6MG/9XvH2xw/Nwoo3jopRPiV2rp6FVW8cRXmDE3mpJrSyXlEQAsCedXNFUdOVfZBvINkldtYKdGUOb3cJR3wEflfhkGc14Zk7Joa1P31TZZcZaN+Um4yvLzXjbF37tugoLe72sGLTibT8IyPJgI13XdMlA+2+xu1h2z31woh2E0OPoguXcdPGAsXHcqwm7Oinsq6u6DXao/sJoWFAaAoZCoSaR9rnRBuh+sUF4OaNUP3iIjBqeu/MJYa8cFvYzkWSCNviTQUhbVYYjZo3Sr7s6pFC4kC7DpvDIzYqCWm59EQDsgLsQqrsbuxYmY9s/8zbaEaN8npnyPm9wrzk1oAu1pLaFr6BI7AxR4K00Hr8CDNUKlWH4i8v1YSfv38S5Q1OjE6Iwe+X5srEX7bEaiQcaxqpVRFfx8iPN8v1z1TOSzVhrd8apzcbmMIp+Jd+V3mpZuxaOwc7VsyQLSPM5xUorGqCRq0KMtMOtGdiOS/WvCVvRPmoqAZn65xY/512i6tCSZPI2VoH3B5W/Lt4U3vHsTTKUFbbghuf39/7DWBhEOqzL9nYfsFz0p/6l36+/l5vov8Zm2ISZ63npMTi+OPz8btbcvHRqpn9Jv66ypBOARNDmGgjMPn7/b0WolhRSqn29FQQpUYWRqOWpUcZjVrRNJnRqLFzzWwUlNXhnte+DHOd1RiXEouvw0hjSyNeW5ZPx+HyBkxOG4Y7X/2P4vJ5qWY8s2wCWM4rRqkq6p1Y+84JcRpPln/ihXAg7syaJpSh8ezMJMzOTEJlgxMe1osbn98PwG+U3Y8jHZXqUt0eViy1iNKoUPDzuWh0cpLRdfJtwHJe3LLxAIqqm5GZZMCHq2dBp2VQ2eBESQifwF9uP4WMJAPKavlJIRajFote2I/iGof43lkWI0okdjNCs7k0mNrftc+hRudVNjhlVjlajQpr3z6Gourm9tGFAzQqSGntnoOfvjJL9n3eNX1U50+MIEgAEkQfIq3vyrWaoFKpxFqvwLrAQMGyv7QOC3OGd/u9w21k0WkZ7FwTPFGA0aiRn5HYYTe0fJ29+NkN2RgRFyN+dqX3lNX9WU3wAaKAk0bzAF5EPrOsPYVZWtMse7y0tgUZCXwDTZS/sUugs47gwAhpYArwqiRj0Pv1NyznxXl/0wajUaPK7ha//zbOh++98iV2rMqHSiVMHlLJxrYdKKsTO45La1tw88YCfLxmtmgcXRzw/QOAi/WhrLYFGUkGvPmja3Hbi4dEwSS8d0mA12CbQsAsy9K/RtCB2/t0dRNiohmYdPJ91MP5xO9I+HyR3t2phDQlP1AFbKTR2w2jvQ0JQILoAnXNTmz+vBwrrk9XdOLvDMEwOrAu8JF3jmPhs/tkB+Zp6fGyEVoPvX0Mxx5feMWm0OEcsEIt15mIDBRZM65K6HB9A2fYShs+SmocGB0fgwp/bZ9U+AmkJxqQmxIrnqCljQqnLjnw+Wkbrh9rAaNRh7SmUZpja9YzQSnA9EQDOK8PmRYDSm0t4kSf/oqqKEWIA4Vbsc2Bg2fqZePybt7IT4ARrF6klNoc4md5/ruT8NBbR1FiU44EltW24EjlZUVjaYD3LvSEyJSONGvxwar+TZNZzTrZOt68sQA+ACmxwcb/aXE6VDa22xHpozSwmiPX4DcQt4fFt5/fL5ZTCPu0EL2myODQhAQgQYRJXbMTU576HADwl4MV+Oqx67stAqXiSqNWicJHGlnQaRlsvOsa/Pjv/CQMV5u3R2YDXykdicjORJb0BCON/LWnDg0ywVHR4ER2shHvLp+hKCQZjRrbV80Sm2VKa5qw4s3j4uM//rt8covUm1O0UNlywt8Za8TLP5iE6/60H3ZXwNzVt48BKpVYvwkAPq8Xbg+LW188hBKbA1kWI7Y+OEP0vxsZp+8RP9FQhEpp71iZj8UbDogp3Kf/VSymxQXxyj8nWJ3lWk2wmnWyTuxXfzBZNo1FICMpBhNGmBSniAC8sFLy/gSARxfl9nst8DfVzTKBKqxndXOw6fpdU9JgNmrxi21FAPjvu8ruHhDRH6GmMbCW9uF3jmP97RPg8/nws3dP8GUAiTF44a5rkJ5o6NV9l4gMSAASRJhs/rw86PYTi3Ou+HXT4mMwbrgRX19yYNxweVpMOlKwp2YD9zaBBugd1Vq1z7Dl8Pp9UzEtPR63v3RIFgksrnF0eLIVGqpYzosH//5l0ONCtEPauatkocL7CCrPB5caMwucuuTAog0HxNmfJTYHJj+1WxRDwnbrSudtVwiV0tZpGTxzx0SxQ/Hr6mZkJPH7VGBjTiD/+NFU2Qzfwot2aNQq2TKrrx+Dj4uqUVbrxLI/H1YUfwKhLCaiGFWIR/qO948Ez3QNxR/8NjaBBtmRHjkTIuxKUdpTVU1iPatAaZ0T337hAHSMCm7Wh2yLETtW5fe7WCd6h8jbYwkiQllxfXqHt7uLw+3B15f4A/TXlxxwuNsjENKRggPVQDew1koY5RbY8ZufkQidlsG2lfnYtXYO752I8MbQsZwXu7+pQVmdZBC7X2PkpZrwyJYTsm7rUHM9AyN/UZLXyLUGWypIB78DkIkhITpXWNWEhc/uU+z0vhI62jdqHfIpHEJavLLR3eFrfnrKFrRdZlyVIG6LvFQzFo23iq9XUuNAloX3KcyyGLBjxQxk+sXmmAQ9lGSeLoof8defuD0stnwVvgAUcLV58dq9U7D1wZkAgFs3HwzZxd/fCD6c97z2JaI1XRPcbv80mWKbA0sioFub6B0G3tmEIPqJxNgYfPXY9VdUA6jE3w5VBt1+aEGWeDvUSMFIR0ixeliv2NAhFXOh6gmFiN62MCevsJwXt2wqkKVnAeCd5dMQZ9CB8/pEP8KTF+1496vzuHl8imgynZNiRBsX3Ljw9G3jcdOEFDEVBgCfn7aJKXmAF5mSyWuIUis3PAjv3dONA6H2jRlXJchq/IRaUiGyE4obciyK20W6LYR0u8ALd14DLaMWl/1w9Wyx2SBnuBF3Th+FN784h68vOTA6IQYfRUBE6XB5g2J9YqiUtZQUsx6MRo2ztY6gC5tISgmX2RxiJF2oI9YxanE+sxRdlBruEDtucY0j4j4b0TNQBJAgukBibAyeWJzTY+IPAOZmJXV4u69Q8kS7ktdauvkgFj67D4s2HPBHiox480fXir5xQMd+d+F44QF8hDFQ/AGA3cXhqiQj33ktid79YlsRpv5uN7x+D3y1WoMPVvFRR8HXCwAe/4Cv95L6OM7OTIRWEk0J1FJtXiDTYsTLd18TtD5WU7Q4x7i30WkZbFuRL94WBIBU/L189zX47RJ5CUOtg58THPjdS29Lv8+8VBMyLEbZslV2t5hyPHXJgdRhejHCXVHvDJoR3B9MS48P8khUI7T4k2asf/beSbCcNyhSGknTnFjOi4fePhZ0v5L4A4C2UFctGBqTqoYqJAAJQkJdsxO/+eAU6ppDmw9fKSznxenqJnx+ugZuD4ucVDPGDeevrscNNyLHf1IRlu0L41mhJm7e+r1Y9Pz+Tk2SO0NpDnKJzYFbXzrEmyj3YMosLT4mKD2rY1SYOSYBAC9enrljouxxV5tXHLNXeNGOKrsbmcmx+OkN2bJlDpc3yJ5XUe+Eh+s4RlTqH58XSFVTK6b+brfid9sbZFiMokAJZPwIM+ZfnYxpVyV0+XUZjRrbVuZjz7q52LYiuJM3UBgFzrWOBHRaBkcemw+LpOM31N4YrVFhy/Lp4m3B/FqIlO5ZNxdbH4wsS5XKBmeQhVJHdFQZ+qfvTIioz0b0HJQCJgg/PdXl2xEs58WSDftxyh8R0WpU+M+j8/DB6mDfvVDNE119v8oGJyxGLY5UXg45Ak1aE1dsc2D++n/j5e9PxtgUc1DThLAuwvOUUrSB864BINNiEE9KhQE2FFcCo1Fj+8p8lNkcOFfPN3vM8tcTCoyM08vSn7ooNTISDSiqbpYV9KeY9WLqVGisEL7D+BgGSzYpj36Skms1YcZVCWLnrZS+7OQWBIq001kw0BYaUsKdO6z02h015WwNsDoS0u15qaaw36O3Meq1eO3ea7Fow4EOl2vlfPj5eyfF21FqiF6BkeoDlxYfg4wkI8pqwxeBoQhsAiIGDyQACcLPhj1ngm7/eklej70+y3mxv7RWFH8AbzI79Xd7UPirbwWdSJSaJ0KdbDqzWZH6lyk1k6TFxyDbYhRTdxftrbhp40HkWk3YvjI/aF3K61qwbsuJkOJUqBsrr2sB5/VBo1aB8/qCug57CkajxtgUE8amKM++rLK7ZenP7SvykWExit+ZOP7L5kCu1YR138rCjKsSwGjU4neoVN+3+c5JWPnWMTF1+P4D0zBhZLwoSktrmrH6zaMo88/P1Uep+7STu7N6ysD6vp6K9AQKo23+fSiSumVZzgstoxY78EMxKk6HUsn84zYvMOWpz3H88fkYZohML0CW88Lu7DjVrkboqKdAVy4KiIFHZPwSCSICmDIqvsPb4RJqvuitmw/ivr9+FbR8G+cLSjUCwam0jsaoKXUjSkWbIFAEv7hA+LFG+RgxTH5CK/KPO2tt40QrkVyrCZzXp9jZG/iamcmxGJtiQmZyLEYnxEDnT43qGH7OcV8R+F1mWIyiSHF7WMx/Zq8ofouqmjAqwSCORBM+Z6D4y0mJxTOflcjqxuIMOlnd3NVWMz5ZOxefrJmN1+6dcsVG3t2lJ2ote+v9+wNpjaparcYna2bjo1UzxQ5mgYxEPXY+NFuxA/ymDZHZHctyXizeVIDalraQy4wcFt2h+Bs33IBda+dgG00LGdRQBJAg/Cwcl4woFdDm4+0/Fo5L7vJrhON5F0gof79wR7eFihSmxceIqbf295JHoKSRQ52WwSdrZuOa334m1rnlpMRixRtHRPNgACitacbad9oLzMMtEuejcPxpx816e8RIN1wftlDfpdvDyj4vwKeqhc8jnZ8s5eW7r8Fwkw5LNh8U74tm1IrTITqLThJ9T3ldi1ieIKTpY6KjUFrbfiHzl3umYOaYBJxvdCm+xoXL7ojsju2s/i8jyYD1t4/Hks3KnpeZFiO2ruj/Tm2i96EtTBB+dFoGhb++IWiKRVcor2sJKcYEISHUYaWYojusywOCU2luDxu0ftLXDrRZ+cNt42U1TtskB3YlsWrUa3HyV9/CwTP1SDBEYc1bx1ERcAJs5Xz4RpIyC7dIPNR6dhfB56ywqgl5VhO2rex4tJhSvdbh8oagpo6f3zhWFsUT6ujWvnMcRVVNyEs1Y25WEm54bp/sea2sF+cbXT1S10j0LpxXvs2X//0Inl02HqOG6XDushtZFgOuHR2HZS9/EfLCLTs5NiK7Y5Xrb4147o6J0DJ83WeglY+UUlvHxuvE4IEEIEFIuBLPPZbzYu07x8Xb+qj2iFCoCJTSe4WKainNftVpGcXXdrg82Hr0Ip7c+bX4fMHipLSmGemJhpCRQ52WwZysJCx6YX+Q+FOitS28rtZwI5rhIvU5K6xqwr+LbRhu0qHa7saIOD0yk2M7fY9p6fEybzQdowoyKRZS2dtX5uN0dRNOXrCjzNYcZAJNDBx8PrkAPNfgxK0vfSHeLrG14NYXDwbNQRYmr2QnG7FjZWSmR4W6zv2ltWLJSanNAb1Wg7T4GFQ2ODEyTi9mB3KtsQDaxxzmpUaWpQ3Re5AAJIgeItCPztUmjwiF0zEo1CYJXZnSGpxQs1+lr81yXpy6aMdNGw4EeZo53G1iE0amxYBnl00UIwXZybGwmnWi+OS8PhQHpJHGJBrAer1BwmfNW8fx2U+vC+tk2BNdkyznxdFz9UHzaQNvZ1qM+LAT02GdlsHxJxbiQFkdAAR1D0txe1jcvLFA/F7HpcTia8mIuFxrLBXMDxDC2VdLbC1iY1ReqgnPLJvY6/OdewreszJJlnXwsO0R8/GpZrz7wAyZybkQFezpkYVE5EICkCB6iLT4GGRZDLKowbl6fhKGrdmNGVcldChGhC5hIXUTaJUSavar9PmB820FtBoVzknGgJXaWnDTxgLkpMQiy2JAcU0zbn/pEKBSofCiHVnJxiAbkzN1Ldi1dg44rw9LNh1Aq7+rtqLR1We1UCznxc0b9stS0KEotTmwZGMBdq6Z3eEJTadlsGDc8E5f78OT1TJRfde1aZh2VQJYzitaqtCJc2CQnmgI+q0GkpdqwrvLZwQJvoGSGpXZAL1zXNaBf9LvfSn9LFS6MPQgAUgQPQSjUeODVbNw8wv7UVrnRLRGJYtKRTMqHHlsAYz64GkQbg+LJZsKgqJuUoTZr0o1isLQdyXxlzYsGh+snoXv/9+XsoYQAKIZMgDZYyU1DmQkGZCZFCMWxguWEIxGjSOPLcDNGwpQ3uDs0ykI5XUtYYk/gWJbz42xGj9imOz2pLQ4OmkOUITf6uINB1BSGywCR8fr8e7yGdBpmQEj+JRgNGpo1Kqg331OSmTWLxJ9CwlAguhhfCreOLU1oLmglfXhpo0F+OyRuUG1fd9+fj/K6+VWKioAKaZo2X1KNYrSyJ8QIcxJicWd09IwaWQcsofHitMbymwOrH7zKEolJz3B+Dgv1QR3mxelfjuUsoAT4zPL2ps9jHotdq2bG3HebqPjosH5VDh/mY925lp77kSXPTwWOSmxOFXdjJyUWGQPJ/E3kNFpGWx5YDomPrk76LGKBteAb4QQGsYmjDCBUQPSKXDLpo6MmN8s0X+QACSIHoLlvLh5Y0GQcJJSUe+URaQEA+JA8Qfw3n1HKi932pQibeZwtXF4/b6pyM9IlBlCn611IC0+BmNTTPj44TmyAnFXmxev3zcVk9OGhZyKoDTBoT+mIKSYokVTayUqGuXmt60hZp92B94rcVbEiV6i+3xcVKN4v1ATO1CRNoxFM2oE/gxSTJE3no/oe+gIRhA9RGWDU4yeCfxm8TiMSWw/2AZ22JXXtYgGxIFEa1RhTY0INDkOFH+BJtHSAnHhOdPS43Hr5oMhO1ufWTax24KnJ+cZH6m8rCj+tAwfdc2yyAVpqa1F0aS6u0SaoTFxZeRY5bOSVQDGJOpRXNOMxRsP9Nnc5p5G2jDWynoxSmK6rmPUmJOVGOqpxBCCIoAE0UOkxcfIGid0jArLpozEXdNGKXbYsZwXa99uN1QenRCDCmkkUBXeDE5Go8aW5dNx8Ew9LLFalNe1IMkYhU9P2TB2uFHR6iXQkqW8riWoFkpIDY8f0f1xUA6XBzf7I5zdnWcshW+E4ddLiof1YXRCDLYsn4Zpv9sjpt8zLcYBHckRCNfwmugaURr5b+y5OyZijd/KqcTWgsWbCvDPhzpuIopEAhvGdj40Cxcuu1Ftd3XajEYMHWgvIIgeQjr/tdruxswx7QdapWaB8roWFEmaMDbddQ1WvXEU5f6IVSvrlVm9hBIBLOfF7S8dCir0DiQw+thZCldIDUsjil3B7WEx+andYhq2s3nG4cA3wizEobP18LBeLP/HUfGxinonPjx5SVZ7WWpz4PaXDnVqEh3JhJouQ1w5gd+jIVoju11S03NNRH2JUsPYWL2WptEQMugoQhA9iDD/dd7VyZ1eZQemRNUq4MPV+Yj2z8uVWr2EmvcrdP92Jv4A4A+35qGywamYik1PNCAzIH2aaTF0W/yxnBc7jlfJavDSE2J6pCFDp2Vw/dhkzL86OWh26+M7TiEwblpY1dSjaeC+Rsmwm+gZ0hMNyPPP+c1LNWFWRqJs7u9ANkUWGsYo2keEgvYMgugnAoUVo1HDqNfixBMLg6xelERAWnyMGBmSTrMIxZKNB8D6gPGpZmxZPl3mb8Zo1Pj5jdky2xrpSLSuEMqPcNuKGT0auWI0any4ejZufG4fKiS1i4E1guHOKo5UenqEHtGO0B0vjaxvX5lPpsjEkIAE4CCA6oMGJkL0obCqSdZlq2T1oiQCpHOH3awXf7lnCjivDz/++5Gg9wIAv28zTl60Y/HGAyixtSDLYsAHq2ZBp2UwKyNRrK/TR6mDRqKFi1SsSmlwshjWw4MydFoGnzw8B996di8qJUbXAMCoVXj6O+NxY+7wAf276OkReoScwFIIYfQfQQx2VL7AoYgKNDU1wWw2w263w2SiGoJIoqPRYUTk0xXxznJeMTKRYorG0hcPotQ/yUAwmWY0atH+IRSj4/WyiFlWslEsdBe8wwKNprv6mQJ9CcePMGPrg723b+46dSmk8M1JicWOVbPod0EQxKCnK3qNIoADjMATdHldS8jRYUOdgRAZDXc+cGWDE1azDuu2nMDJi3ZEM2pZfV0r68NtLx3Cxruu6VD8pSfEYMOdk3DTxgLxPmmhu1L0MZx1k37H0oiV1axTnJ3a09tmdmaiKDYDOVXdPCAL+QmCIHoTEoADBJbzoszmwNLNBf4UnQbHHl8AzttpAHdIwnLtg8/zrKYB2wUqjaYJg+kBZYNjYYyckCoOJJpR4cNV+dBpGZldTXcL3TvqThWErVLDSW90tQpdj1uPXsSj24uCHrcYg8fvDRQ6isoOhIscgiAiExKAEY7bw+JAWR3+8PFp2YQJVxuHd49cwDtfnhfvs5q0SDJG9cdqRhzldS1iZ2xhVROKLzVjVLweH5yoRoZFj5IaJyaltY9Ji0QC5/sW2xwYkxiDM3XyLlAhyib49W1ZPh03Pr9f7ikIPkpoc3hwVZJWVuieYopGQVmd4nzhjsSFUmOKNMoWKPSExhPO6+vwed1Fp2WwbOpIvLy3DOcC6gH/U9GIeVcnX/F79DXSiQ7CRZ+wjdweVqzlHMgXOQRB9A8kACMQ4cQbH8Pg2t/tgYdTjvI9vuOU7HZVkweTntyNwl8thFEf2REPt4fFobP1SDHrkWHp/ckKt754UDFqljPciB2rI8/oVamODgAqAsRflBqosruRbTFiy0+mg9GoUdngDBJ/AKDVqMRImFDoHkpghBOlkzam5KWawHl94qQRIFggLtlYgGKbA3mpZrH5RWho6YnaQ+Fz7XxoNib8ehekyeCUAWoGLZ3o4GrjRF9IlvNi8YYDonl3YVUTlX8QBNElIuusR4ipy3nr92LKb3eHFH+h8AH468FzPTJ2q7fgRccu3PfXr3Dj8/uxeMN+lNY0d2md3R4We4ttIUc1pScaMDqhPa0ZaibsqUuOiPRVC5zvKyAVNRoAwkCMYpsDVXY+6pUWH4O8VPmIKwDwcD7c+uJB2fesJDAC3z+U95xQ67dr7Rz4fMDCZ/dhqcSjUDqiLjs5VkxfF16045k7JmLPurnY+uBMsJwXk578DPe89iUmPfnZFY/fMuq1OPGrhRjl3/65VlOfCaOeGnsnvM7ktGHQR/HmxDpGDUtstHiBGDi5hSAIoiuQAIwwpKlLtgvaT2p++/SuEplZcKTBi472dfv6kkMUD6U1zXB72A5Pom4Pi4m/2YV7XvsSE3+zS1EwMBo1PlrVbqocipwUY0T6qglj5ToizhAlLiP1h2M0avzpO+MVn1MSMBtXGBkFyI2nA+cLh/qOhGifUE8oNCIJj21dMRN71s3FjpUzZa+XnmgQR9KFEqFXglGvxe5H5mLPurnY3gepUZbzorSmGUv9Zt2LXtjfZSEriD6Hy4NFL+zHvPV78d1XDuO5OyZg812TYB2mw7dfOIAlGw/AataJBsYAkGM1dXtcH0EQQxNKAUcY3W3qUANYNT8Dz+8uA9CztVU9TY5VeZ0KL9qx8Nl9YsozO9mIHSvzg+rSth69KJoeu1kvDp6pV6zvEkyVtx27iF9skzcGPPbtsZiZkRixNYCMRo1HFmbhh69/Jd73PzeNw68/+lo0Oq5raUNcTBReu3cKZlyVIJsxfKFROaqZZTHIxJzSyCjh/bcsny7eDwBnax1dajYITOuG8rILnFsqvN+VEk6HdU/A1+IVoMQf4QT4hpwlGwuwY1W+Yhe0FKHB66G3jqLE1iLr8C6qapKNuwP4rubiS01494EZYg1g5O3BBEFEOiQAIwyNOnCQlTJq8FE/ISXIARiXYhoQEwN2f1PX4eNCNKi4xoHFGw7gnw/PAaNRh5ww0VF9l07L4PYpI/Hm4UoUVjXBYozCP9fMQmJsZH43UmaOSZAJo+9eOxK3TErBDc/tR02zBwBQWuvEfX/9SqzTYzkvlmwqEDuCpWQlG/GBQjRMyfqF5bxY9vIXfH2f1QSoVCgMUQ+YYoqGCnz5gcp/W6m2MJQACiVCBwIs58XiTXLxJ1Bsc4jbIlQdpbRbXSBUuYKU945ewPXZySixyWsA0xMN1BVMEERYDJwj7RAhPdGAvFQzChW83rQalVgTqHSK+NMnp/HRQ7M7jTj0NzfkWPDzrfL7Hl90NbYfu4jCqiZRTABASW0LymwOaBm1rHtUIJz6LqVxTwMBJWHEaNT4633XYs1bx2Q1YCf9qddVbxwVa+0Ecq0mPHvHxC6NtZLWAErFyUmJ16RQi3auvkXcXj4AX55rhK2pVZbWPXS2Hs/uKlVsKmE5L6rs7m7PHe5TOBZorADiRgMahq/FCxDbaXE6VDa6kZ1sFIV4qIh8ZYMzaI6z8DtPMkShtqVNcTX+/sV5/P2L87Jjwop/fIXoKAZFVU09Zq9DEMTghQRgPxLKRHebP1VmMWpx20uHUFzjQJbFiLuuTcP/fPR1yNcrq3PifKMrojsBWc6L7//lq6D7//jJafzn0Xk4dt6O+/4qf3z1W8dQGtA9mpdqwjPLwhc1fZUO7Gmk0TlpBDQv1YxP1szGz947yUfmRvD1dVLxl5VsxKa7runWPFOrWSeOhdMxKoyIixFtiB555zjefWCGGCEcFacXZxHro9R4aufXKKt1ikJeH6WBJVYnayopszkwNsUUsts4Iv3tOBb4vwVA1THAOgn40WdIi49BpsWIUsn3rlKp8cma2Ug0Mpjxh3+jjfNBH6WBVSFSnRYfI+7TAL/NotQqnKpuRpJJh7qWtqDZxlKkTWKlte1p/0guASEIIjIgAdhPhGOiCwA7H5qN8roWPPLO8Q7F30ChssEpTi6R4uF8WLrpEF78/uSgx0ol3aO71s6BRq2KLGHQR8iichft0DLtFwtCul8oAVCqn+wK5xtdYqOOm/XJPCgLq5pwQOJPeK7RBa1GhT/fPRn/+2m7X6UPwG8Wj8Otk1Lx3VcOy15/8cb9+M+j83HiQpNMGAppzJ42iu4RGit48QfwfxsrwCRmYMOdk3Dj8/vFxc41OLH871/hkt2NNr9Ac7VxqKh3oqbJjQkjTDhxoUmM6m6TeDICfDc1AHxd3YyPVuWj8KId/zhUgVOXHKLQ7oxILgEhCCIyIAHYxwiRjXDNcBmNGhq1KihNBAC/vSUHk0bG4efvn0RRVRPyUs0R3wkoWJQoicDyBic0ahWyLAaxtkmK0D0aEWKgi0gjWgC6Fd2S+u4JJ/jAyGaoRotQ66K0DMt5cb4Ta5wVbx4Fo2rvVPdwPjQ6PTKhCABP/fM0rk1PELuEBTwcRI9LqahZ+85xPLwgs1eMoq+YuNF85K/qGHwpk1DWlgDUNGNYTPB3eE4yaxkAGBXEKT4CUt9FIWrv9rBi6nj8CDMyLEbERDNYOikVVXY3rGYdzje60NrG4pF3TihawWx9YDrGj4wbkL8TgiD6DpXP5+u07bQrw4WJ0MhSeNLC+hFmbH1QXhMlPUFLC8XbU218c4B0wsJAiYqxnBfFl5pxrLIRbxw+h28u8RG+vFQTtq3Ih8PtwcQnd8ue8/CCDCzKsw4oASid4RtuQ0W4r9nRtu5sdFhH0TXp49K0rqvNi1Hx+iBhIxDNqHHksfm489X/BIn7zXdOxHO7SxVFfWfkpZqxLVIigADAsWDrz+K2t6pwotpvd6PqmmWTlNfvm6qY4s+2GPHO8mm448+HQzaRsJwX+0trg0omhhujcOAXoZtuCIIYvHRFr1EEsA8pr2uRFdYHpjOF6RjrPy1GUXWzrLOz1e+Jl2ExYt23ssSDvpA2i/Z7uQ0EWM6L77x0SGZwDABr5meivK4FZxQ6Kj88fhHPfVY2YEZeyU7mkmaAwIaKrka3OqtlFEy2+XnRahx7fKFMBHY2vk36uJv14i/3TMEzu0pQVNUUUvyNitNh50OzAQB3Th2Je2ekYd17heLja989gVbWh6wkA35/Wy7WbTmJigaXeBGTa41FWW0L3G3Bqc1nlk2IrG2tYVCpsuJEdal4V6D4M0Vr0NTK79tRat6sO9caizO1Ttk+H2h5I/3ui20OLH7hACrtrQD4bVVQVidrlGE0asy4KkHWCAIAlxxtKL7UjBwFM3CCIAgBEoB9BMt5sebtY+JtfZQaI+P0srmewolbQBB3K944glJ/9KSoqgkpZr0YlQGAxRsPoM2LyKqX6oBDZ+uDxJ9Wo8L9fzsCINjWZVi0CmfqePEhzPWN9JOb7GRe45B1NgvbrjfqtPjvlt8vXG1eHDpbj+vHtnskKqWRpQQ+PjI+Jih9G8j/LMkFAOT9epdoBSOl1a+QSmpbEGfQ4bN114mR0Sq7G61tHL79woGg181LbTc3jqSmkMDGjcC6PEH8Abz4e/2+qcjPSATLeXG4vCGoBlDAYtRidLweFX6hLYg/gXte+zLoN15e16I4LejkhcsR/xshCKJ/IQHYR5yutuPr6mbxtqvNK+vYPVBWJxN/AF/z1trGiuIP4FNtnNcnW1b4b0TVS3VAilkfdJ/0JFbtH2kmcLlVfoK7ZdMBHHt8IWwOT0QIAiUCRYL0E7javKIo6Ol1D/xuA28L0zlCiSnh8TKbA9V2F5KMUci2GIOsZbQavo5PH6XBjKsSsON4lcwKRo12q6JAwcto1EiLjxHXQdoAAQCjE2Lw0t2TkWHh9+PSmmasfee4WOfa3ylhwVZIWO+RcXqcb3ThzS/O4bVD52TLZifHYlp6PBiNGoxGLaZ752a3X+QIRtCBNYJKnLxox/7SWoyIi8HIOD3WvHUsaBkVgJvHp1zhpyQIYrBDArAPqGt24s6XDwbd72xtw95iGyanDcPv/vmN7LGN352E7JRYVAScHFtZLy6EKNDPSo7MsWaBZFiMyBluxKlLwanecGjzAjdvLEB5vRPjhhtxY24KfjAjDcMMoQ2h+xpGo8Yzd0wUOzqlZCUbe83zLsNiRK7VhKKqJuRaTaKIClw36UWCtFaxyu6GxajF0s0H4WrjxMjl6IQYVNS373dv/3g6mls5MYp1Q44F/721XehKZcy2FfnQMmpZTestmwrEdXzvgRmiWB4dH4OPVuXDqNcqGn8XXrSjtKYZV1v7N7rFaNQyu6XM5FisWZAhE4Aj43QormnGspe/CGkCXV7XgrVvH0OR5OKwI6IZtVj+kZVsDGoCsZqi8c81s2DUa7v70QiCGCJQE0gvc7nFHdTQICDU7igVkY+O06Oi0YVcqwkll5rgkZxRMxL1KKsLrsf6ZM1sjE2J3O0T2AlbXteC1jYWq944hopG+efJSIzBf//X1fj9P7/BmTq54BXqqgL54hdz4fQgYqKCLOfFvPX/RqWkdi7FFI3Pf3pdr067CKfLVxq9EhpUBOPxQLGnhMUQhT0/vU4Uaks3HxSbP6QpUSUT6tPVTTLblI9W5SPDYsSSjQUotrU3PFQ2ODFv/d6g985I1OOTtddFxDaW4vaw+Ly4FifPN2L78SpUN7WncF+7dwpmZybJGr2Uptp0FSE6m2Ux4IU7r0GGxRhx3wtBEH0HNYFEEH87VKl4/6j4GJzzR/KUOggFQVRU1YRXvj8ZP/77EfGxsjpXUEdmpsWgGO2JBBwuDz44UY03Dp/DqepmMY0n+L1VNLoQpVGJnmkC9//tCHJTYpGRZEBZbQtGxunxi2+PxabPy3BKIWIy64/7wHp9/VYLGSi8HG4PLgQ0Trx6z9ReH3XWUaNIoFjLSjKIUSRh6kxFvVMUg0IEMNdqQklNs5iqt7W0YfJTu3HiiYWosrtlnb/SejgPy2Hhs/tk26TaLv9OVr1xFC/9YIqYZhZKGaT1iFLK6lxBDRH9jXT0nRL3/fUrWfpaWiPaXYTo6UByAOgJOupyJwgifIbGEaMfuS5gxqrAprsmIZoJ/fVnJ/Mn8PEjzJidmYg8SUF3XqoJO1fPQpZf8GUlG/HhqlkRcQJwe1jsLbbB7WEB8OnvvF/vwqPbi0TRJqTxpCfBQPFX5o/6FVU348W7J2PPurn4/KfXYWyKSVH8AQDr5V9DEBB9iRDRmbd+L27dfBAOlwfX/m6PLBWqUQFjkvrep1G6TQKNuEtqW5CeEFw2sPXBmXj9vqko/NVC7Fk3F9tX5uPoLxcg2dieWmxl+aYGwdtRQB/F74fZybGi9Yt0m0wdFQetpr1VpKLRhYckDVIZSQbExzAoKKvDm/dfi49W5Qc1ltzz2pf4rxf243R1E1jOC5bz4mytAyzXuUlyb3C4vCGk+BMolHwHFqNW/N51/uOAjglvDriAz+sVPRv763OHS09tH4fLgwm/2YV7XvsSk578TDzODBQCj48DBen2U9qW/f37I7oHXT71MuOsJlxticE3tnZBkpfK12a9u3wGfvL3L3GpySN7Tl6qGe8GePttWzFTTNsJ6bR/rpkdMZ2RgDwKoo/SYP//m4Npv/tccZTVg/84gu0rZ0LHqOBWCIEKkalA82dpVCgv1Yw2jwena/mIkuiN2A9TEKQWPycv2vHhyeqg7kzOB1TZ3X3apBO4Tb58dJ7MiDsv1YS//3CqaMoMADkpscgeHoscDS/qhHoyo16L3evmYvJTu9Hq96IUGhyk++fIOL1YSzj1d7tFSxqrWQeHy4ObNxbAw/nEqG9mUoxsnm5ZbQsmPblbHCO3bcVMxX2opMaBG5/fj3HDjWB9/O3+iv5OS48X979QpMXrYTXr4PawmPLUbrhZLxgVcFWSAV9XNyv+Djri1CWHbFsIptKRRjjek+Ecx1jOi5s3FoiRalcbh8PlDWJjTaTj9rCY+JtdcLNeaDUqHP3lAlmtplCawXl90KhV/e55quhjmmoGfD4U+udNb1k+HeV1LVjz9jGU2FoGjBMFwRN5R4tBBqNRY9uq2bhpw36U1TqRkRSDt+6fhttfOqQ43eN3t+Ti1mtSodMyMqHAaNTITNQD9WVAPYCEDDAaJqI6fqVREFcbh0UvHISCQwUAoKLBhQXP7FU86WVaDPhg1SzF1FZgFyuAIOHR14KY5bx4ZMsJ8XZeqgnjRwwLWi4v1dTnwjRwmxypvBx0MVHZ4JSJ1fXLJob8/ox6LU48sTAoBRfYFJEWH4P9pbUyS5riS01Y9ufD4gm8jfPh1R9MxiqFTlZhbVxtHKrtLllHtZCeFvha0kwkWCf19bhAnZbBsccX4HB5A7KHx2DWH/YGlXZUNrhw+8tfYM38DDFNzvogcwdQ4i/3TMHjO4pw8bK8O35UnA7nGvn7IlEMCYLmQmNo78mOxGGgMKxscKJcUpsazagxOW0YztY6IuYiuCMOnqkXt7uH82HRC/vx6r3XilZH0tIMAP3qeRpoSl4sGccpcPKiHTc+v19WLzxQnCgIHhKAfUBFvRNl/kHtZbVOvLa/QlH8RWlUeHR7Ed7+8nzwVRTHAq/OB6qP87dTJgH3fwZoImcTSqMg0YwKNc2tHS5f1xKcBhkdH4MPV80KEsBSAmvcpMKjtw88StGKwLTqM8smYmScHtEaFVo5H6LUwPaVs5A9PLZfI1PSiJ0g/IDg8Xw/e/dEhyceRqPGyIATbmCDj1KDw4//dkQm3ADgYqNL0QBaqD0UbGaktise1otFG4J9AwG+HOKRd46LEYo3778WRyov90m9mE7LYG62hU+FhbjwKbxoh6cTqxcASI+PQXmDE+NHmDEnKwk7V+eLUVEV+BR9psWAqb/bI9u2kYLbw+LmDQdQ6q8vlVoBWc06UbSFMiZ3e1gs2VQgm4Iijf6nJ8Rg24oZuOvV/0TezOgQBPqbnmt0Y+Gz+5CdbMT62ycETdAprGrqNzEVaEqelWxESQ3/V6tRo6iqCdGMOqhZLDs5dkA4URA8kaMeBjFna+VX+Ov3lAYtE6Vur4NTvIpqrGgXfwBQzQ+jR2JGz69wNxGiIIfO1uPJHYU429ixAAwkI1GPjx6aHZFpLCB0tCLQPDk90YAymwOt/u3Z5gW0jLpfTk7SyJQggpQ+x5++M17szC2sakJ5XYtMWAu4PazYrZttMWLHKl4oSl9v/bIJig0ONocn6L5fffi14nq//8B0lNpasChvuLjOQlQPAMYNN8oifwD4TtjvThI/x8mLdjFFKoyq6wt7FOn+kGM1oVTSPAMAz+4O/v1LyU6OxfsPTBeFK6NRY5hBh8JfLcTOwktYlDdc/ByB2zYSYDkvbt6wH6W17eLA1ebFH2/Nw6K84WI6UUghSks6OK8Pbg+LxRsLUBLQFHRVklEW/e9sqk2kkZkci6uTDfimRm7dU1zDe0AG0h8ZAwF5qY0JXn99dUmNAznDjfj90lz8YluR7DnRjBrvPzA9okU4IScyjhiDGJbz4g8BHn+B3DdjFNZ9K6v9alaphi1uNJAyUR4BjBvdC2t8Zei0DEYlGDoUf2MtepQ3tIrRIKs5Gi/dPQXjrKaIPHgI0S3O61M84SilpVcrpDX7CyEyJRB44iyva8G6Lcdlz3n4nePYERAFZDkvH5Xxn5iLbQ4s2VSAjXddI3s9AOLJIzPJgDavr1NbGSkZSQY8seMUiqqb8Y8vzuGtH0/DbS8eEi1itiyfDrW6fb0EOyUto8HoBHmUSEgZtrJe3LyhALvWze31fUy6P3hYr8zyBgBKJaba/n4Z0dYoMykG7z8wXTGyZdRrcce1abLXCty2kUB5XYtM/AH8Nvr51kK8uPeMuC+cvGjH+UYXtvpLEh555zgWPrsPWRaDbG50psUIzusDy3ll0f/OptpEGg63B6drlOdhBwaF/3LPFMzJSuq34yGjUWPL8uk4XN6AZJNOtg+fuuTAL7YVyaYbAfxv7EytA7F67YBIyRMkAHud8roWVF4OLYZUANZ9KwtGvbbDCQ3QMMD9u/kaQABIyIio9K+UtPgY5KbEiua2uig13n9gJtQqiOlHh9uDvx2qxNysJOSkmiP2YMFyXizdVIDCqibkpsSKqdLAE470xFRa0yw7yUcz/Ni/SCHwxMl5fUGd1aeqmoKsViobnOJMY4HiGgcuNDrFGj0hArpl+XQxUpiTEou0OD0q/dZGOkaNuJgomU+ewKg4PZ5ZNhGLN/ERkcKqJsxbv1eMHp68aMfh8gbZeDohulZU1SQKisoGJyxGrRgBBIDyBmefRYmEyPAtm4IjO1ICT/yb756C6qbWTiNbkWyFEtgJyqjat1FFvVNm+bT27WN49ruTALTPyZaKPwCA1xtkJQR0PtUmkmA5L27aUKDYzBRIlBqYOSah32r/hN/OrS8dQkmNA7lWE3JSYoOOEUqfZcnmQwAGzljSoU5kHTmGEN+bNgIZSbG4ffIIMZ3TkX8bAF7wWcb20Rp2H0ajxvZVs8RxYjOuSpCdpNweFjP+8G+42ji8uPcsjj2+IGIPFOV1LeKJqai6GTtXz4Jeq+nSCaeV9Ybs/u2PGbfSE6fVrMN3XjoUtIw+Sh00e1YqHIVGDH2URvS4+2TNbGjUvJVJld0tRgoDTxxu1isTf5lJMXj69glY8/ZxVDS48NP3TsiWl6aOs5ONmJYeL7vAUPp8wnd95LH5uHlDgVhP15dRosoGZ6dzlKWMjtcjxRSN775yWLxPKQ0Y2NkdKd2/QtPHz96Vb7/Aekip5VNRdTMWPrsPmRajuE2lc85HxelRWtceMQwUw50eM3uY7v5eKxucuBDQxBOKNi9kY0L7CmlpSDSjEmd4F1U14c93T8ZP/nGkk1doZyCk5AnyAex10hMNokea4I82foQZv16ch/tmXTVoRzYxGjXGpphw/djkoJNTYGfq4fKG/ljFbrH2nWMdTtg4W+vAyDg9clLaD96CBYrS8oJ34KIX9vepN5hw4qyyu4NEyrysRPEELKSIhedsXTETe9bNxYknFuL1+6aK27Hwoh33/OU/WPjsPtyyqQAWozakr53U/3LEMB3e+sk0/Oy9k6jwm2aX1DgwWkGojRimF9PS627IDnp83HAjRsbpZX5kRr0Wu9bNxZ51c7H1wb6NSAT6I3ZGRYMLS188KNsev1+aF7TOkfj7Efblhc/uC3usnZRSmwOtnBev3TtFNg/5XKML+igNAPR7mlcwUZ+3fi+Wbj7YJc874eIpXDhv1yyBegJpaUhrgGq3NYcnXgX6e1sR4UECsJcRPNL2rJuLY48v7JcTUaQhdKYCiLjuxUDSEw3ISm6/ii2xtYjds4HmqEs3FWDe+r24/aVDWL9sovgcV5sX5/3pT+lzZJ12NQ4s2VjQ50aqafExyLXKxwXtKamT3V77znFxvQThyGjUSDbpRDNyAGLXd1FVE/aX1Sta/IyOj5F1Al+47Eb+H/fK0n7RjBrbV85ApuS1tRoVPlkzCwDwX8/vw31//QrRAQLzf78zHste/kI041Za575E+O3vWjsHn6yZHfQ9K1EakP58ZMvxoH0iEn8/PTHZpNTWAkusLsiY3NXG4fX7pvb7cbO8rkXs1C2UXBiFg1BTJz2WdMRP3z3RZ8cC4ZhkNesURWqUGvjljlPQ8rscdFGht4GOUeOTNbP7fVsR4dH/eYMhgDRNQSFx5c7USIXRqPHByvz2ObX+K9vATtr//c54MVUs/JVaq/zotcPYcNc1eGxbEYqqm5FnNeHdB2YgO9ko1tUV2xx9ljYRBKhJp0ZDCy/cTNFqNLUGn3SKAuwoWM6LWzYViJEqS2w0bAGWP09+JO/uVQPYtmIGxiQZxYYOgUBrmFbWiwYni4/XzEZpTTOq7W6xJuq/XtgvisXAKEW1vfPaub5G8EdkOW/QNBMlclJi4WjlxDGRwgWH9HNE4u8nLT5G5tXYHTKSYvCdlw7KIoAA/zvq77F/LOdFRYDg87BdE2hVdrfM8LwjijroxO9JpDXOwjHpfKMLa98+hqLqZoyK0+Oc/+LVw/FlCh+tnoXqplacb3Dih69/JXs9N8t365P4GxjQViL6BaF7MRJOXp2h0zLYuWa2LHob2EkbON9Wo1bhT98ZL96uvNyKJZsPiemxQn/Dwo6V+ci2tI/964u0iTT1POWpz1Fl58WbkvgD+Jmz0vUqr2uRpSkDxR/An+ykeAE8uq0I333lMIptDmQkGcSSCOlYOICfky2kwDKTYzHaPxGhssEZdAJNG9aeWn9+dyny/FE26XcZCWOqKhucIcXRmAQdPlkzG5+smQ2oVKL4A0JbgUTa74fRqPHMHRO79JwoNXD18HZh62F9QeIPANYuyOzXbSj8XqTz2AHg4XeOhb0+LOcF5/UplgQIJ+HR8b3fKBb4PUprnIVjUmZyLLavmoU96+bipe9Plj2/osGFI5WXkZ5owMwxCci0BI+27I/0NdE9IuPoQRARjLTjUmkk3fgRZkwdFSczXB4ZpxfTvqFgOb45ZMeq/D6dYBJuum5MogFn6lrg8ae4O1o3NSCbezw6ISbI+kXaDFJW24KPVuWj1tGKP31yWubpV9PkEu1AtIwGRVVNyE424v0HZsiiqlqNCpWSwvqiqibsWjtHNgVE6lvYn52JafExsmivlIcXZiHDYkR5XQtOBYjEZzqYyhJppCcagixcOqLNC7g87aPzKhV+L/ooDX74+lftYx7925DlvDh0th4pZj0yLL2b3g/1eylViM4qIc0W5FpNeHhBBp77rEx83Avg1R9MxqyMRNz+8hco9HsiChNCwqGjrnChOcfZ2iY2WgnfYyiErBXLeZFrNYkXfEJzmDASLrBkAQDWbTmOHREym57oGBKABNEBHXVcrl82Aa1tHC41ufFFQGF+ld2N9EQDcqwm2UldF6WGu82LnJRYcX5mntXU5ejJldCRGJFyxp/yKvHXJ+5cM5ufAhKnD5rh7IVc9FXUOzEmMQYXL7vE5dLidKhsbBds0VEajEowBBk6C8tLhYRglvuzG64G5/XhD598g3MNcsEgnRvNcl6U1jRjxRtHRUue/kwLMxo1dkhKCaSsfvskcvdV4OnbJ8juz7IYuyQC+htGo8bz352Eb7+gPKVFiYoGV9BoP4Hf3pKDX24/BQDib+vkRTvKbPy+IEQLc60mbO/FkWlp8TFINUXjYoBt0ej4mLAi9lIBWVTVpNgZ/r+fnMZ1a+ZgWzdsbaTHKK1GhXeXT0dO6jDxd3DLxgNBjTnCb0FoUgwlOhmNGttX5qO0phnHKi/j0e28+XPg1BIpp6qb+yR9TVw5JAAJIgQs58WO41VBHZf5GYniQVVqhqpj1HCzXlmd4NoFmfC0eaHR8FGp9EQDztS24Cd//0qc7VpY1aToc9ZbBIoRjQohZzYLFNscKL7UjKKLdowdbgxq8Bg/woyH5mXg/r+1p8nO1DnxyZrZuNDoxB8/KUapzSF+R3mpJvFkI0RShSiPYOwcSGmtM6QVxUvfuwbXZSeJJz2lUXQAYDH2X9e9UEpQXteC+//6pVhbBfDC4EKjUxwfqNWosPXBGQMiiiK1RslMjpVFacNBSfwBwJuHKsTXEiOAI8yotrtkqeIiBc/KnsYcEyUTgIwKePS/ru40Mg7IswWhkNZ6dvUCRdoV7uF8WLL5EHKsJqy/fQIuNDoVu7LTEwyiyOxIdArRw//3fqFsO1ydEotvutHtTUQWKp/P12nCvqmpCWazGXa7HSZT551sBDHQESwfCi/aZXNpjz2+ABX1zqDpDgKv3zcV+RmJYDkvJj25KyhK4fawuOa3nykKHADYs25un0WopKavS188hFKbAyPjdDjfGGz5kJNixKlq5YjhK9+fjNGJBqSYojHlqc9EcSh85soGJ+at3ysuL3xHwslGOMm0trG41NSKCw0u/I+kicRiiIKtpa3Tz5NnNYkzgxc+u09xmdfvmxoRkzMcLo9sP5B63wl8/NAsXG0N3zqkP1AaK8hyXhwub8CEESYcO2+Hh/Vi+T+Oduv1P1iZj0anB5PThsHm8IgXVtLflhCNFiLpQhS4pyitaZbtT8N0Gjg9HDxehO3DKOzjwpzqvFQzfr80F49sOY4SWwvGjzB3u3PW7WEx/tf/CnlMCSRKDRx7fGGnFmShooedkWkx4OM1cwbExctgpCt6jSKABKGA1PLBByDVHI1PH54DnZYJavgQyLQYREuOrUcvBkUpyuta8JPXv5IdqFOH6RAXo0WRf4pGX3pnCebOlQ1OfOivQ3R5OCza0J7C02qADXdeg5MXLssEYIpJi+omD3KtsXhhT5mYQhqTZMSp6mZkWox474EZirOSlSI1j2w5IYsaCaJbBcDW0oZMiwGVDa6Q0SKAj6R+XdWEx7YVKj6u1agiwjIF4P0JT/7qWzh4ph5en08WORWotrsjXgAqjxU8IROEQHuUN3B8GMDPAFep1Yr1ZEs28dMzMpMMeOHOSWLd7JePzseX5xrBedu/u56OpAui7eG35OL1sru9btHVxmF/aR3GWIwdpm2FbvBt/gsiYdl/rplzxUbwOi2D/zw6D1Of2h00WSaQKI0Kh/77urD8Z8tsjrDEH6MGZD/LzmNKRIRAApAgwuCivRU2hwdGvRZTR8WBUQVPOCi1teD2lw4BKpViCszl4VAu6fCMUgNPLBqH/IwEMboBAGdrHX3SEKIUvQH4k21pLX8y9nBQjN78ZM4YzMpMAgAxOiL9zKU2hzj9pLORXZUNzqDvywfgpwuz8PSuEv/rtWDn6lmy1LkS33npoExgZyTqUVbnwqg4HXY+NDtiumYB/sQ97+pksJw3KEWoY9SYOSahH9cuPALFPQBFKx5h+9udbVj64kHZa5TVuZBrNeGTNbOxbstxWbOQsCVLa1vw7RcOiJFSIdqXYopGtsUoq6vsiVrPjsoIAln15lG0cr6whGfg5JKemmQyzKBD4f/cgINn6sFyPlmpxKh4vVgv28b5cOcr/8Hm703uNFJ6viG8+d3/+OG1eGLHKZT4jxmltX03cpG4MihGSxAKpCcaZNM8ojW875fbw+K7f/4iSPwJFFY1hax/+nexTbQpsZq00GjUWP7GUUx+ajcsRi1YzotFL+wPMjLuLQKjN5UNTjAaNT5cPQvZHRjWqgD8+qNv8MiWExgZpxfNY/NSzYo2LIDcjNntYbG32CZOPlGamJFrNeH+2enia48fYcaYJANevvsaWM3RIdfNw/mQ7n/f8SPM+OihOdizbi52//T6iJ26I52wUvQrfsLK8ScWRpRYDYV03bc+OBPpiQbZNhP2AWH7G3XKn6moqglaRo3n/HOBQyFE1YVo39Tf7UaxzYFR8XrkhNj3ukNXjK1b/Rcc0qk5/YFwQTHvagty/ceuTIsB7z84HanD2i1mSm18iYRwjFGy2GE5L57/rCSs931ixylsXTFTPGbQFJCBA9UAEkQITlc3BdX6BUYbAMBqjkacPgqn/N2sQlej0PAgJSclFh7OG5TuspqjEaVWyxoDerseUBYBDKhBEtJfgiGssO6NTo/oGwgAu9bOQXqiQYzuAegwpRWqq1ro2n3o7WMotbUgL9WMbf6IpDCzWLDI6Ah9lAZfPjpPjKhSHVLP09k83I4eDzQRF5Bub2GfHB0fA7WPw9nG9v0tVMcwwEeuN9/deWQr3M8oGCR3htC4I6zDh6tn9at4l9Yv56TEorSmGZ4Q15K71s4JStkzGnVQ3aOUUXF6MGofztS7Za+TnmgQBXBP12ES4UM1gATRAyhF4IptDmQmxaC0tj09MkwfhbXfyhZrkVpZL0YnxOCjVfk4U+vAe0cv4O9fnAcg98KTIhVVAJCdHNvrV9EdpWaFmqXtq2bJohpKJ4XANFZHolVpju3cbAsYjRrRURpRGBdK0nhXJRlxttYRVmfpthUzYdRrIzbaN5BxuDz48GQ1/nGoAqcuhfZV7CitKdiKlNe18NNRVCpo1CqZYJDukyznxeKNB1Bia0GWxYCtD87EntO1eOid40GvXVrb0mNTKARj61AiSEqrpOSgtLYFN2880K9NENKSilDHG4A3GQeCU/Zp8TF4ZMuJkM979d6pGBmnx+INB8S07yNbTuDd5dMVxSQRuZAAJAgFWM6LR94NPghmJhnw7B0TcKHRjQfe4Gvjvr7kwP1/OyKLTlTUO3GuwYVfbj/V5fFY6Qkx2LGybw6endUgCUIQ4L8TqcVHrjW2yz51whxbIQIobcoIrCeTCuC0+BhkWgyKjQIC40eYkWGhuqPewOHyIO/Xu2QNHELKs6t+b9J9KtTjwj4Z2CjhcHtk4o8BwPr/n5fas6nH9ESDzARZqYFFiVJbS7/64El/RxZjFGwO5Q76Z5ZNFFP20t+cUk2uQKbFiDM2Pl38wl3XiBmSwot2vHf0QsSNYiQ6hlLABCFBcNS3xEbLDG2jNCq0SZsLkgw4V9/SYded1RwdFNnrjKxkIz5YmR+x9V9Cahjofpqns6kFodKHu05dChrHBfBC9Nk7JlHaqRd56/A5/GJbUdD9o+L12Ll6Vp9FXDfuLhUbgwDg4QUZWJRnBdA7acfPvr4k69AOnHgTiv628JHO+p7y1OdBj2dZjPin39g98Dfn9rBY9Pw+nKmXux2kmHSobmpP++akxOJMrUPmCSo06VyJrQ1xZVAKmCC6QaCj/riUWHztT6G0BXhsldUGR6ICI1RV9lZEqdGpNQPQbnORmRwb0QfNzqI34SDMsQ31+qGiBrMzE8W6yig1cOgX16PJ7aVav17G7WHx6v4zio+da3Bh0pO7UPg/N8jEfGd1gt3l7ukjZQLw3hmjMMyg6+AZV8aIOHlEMdy2rP7eH6W/o+OPz8fT/yrBPw6fFx//03fyQtZv3vbiwSDxF6WBTPwByullV5s3yOeTiFxIABKEn0BH/frm0HYjSnh9vqDGjzYvPwINUAXNOn31B5OROkwPRqOm6FUY6LQMjj+xUBY9TKRpU70Ky3mxZFMBztSFnmvd5gX2l9ZhYc5w8TmB9kI9tW8PM+hw/PH5+McX53H39JG9Kv4AIMNilKWBBTpqRolmVBgZp1d8rD8YZtBhbpZFJgBtzR4Awdtq/bIJisKujQu6S5Fcq4nE3wCCBCBBgD8QJhi0sjFkNQG1M4kxDH61OBcv7ClVrEU7U6vsm1WpMFkjL9WE6/zNDx3RUbp0KNJR9JDoeSobnJ3OjAaAP35yGrMzE1FR78SxysZerQUbZtBh1fzMHnu9jpA2rQhTPLKTjXj/gRmobmpFRV1LUFlCK+sTPTAjhREBglS4XV7XIttWJZeaoVVD1jXMqACNWiVrdpHy65vHITVOj9Rh+ojPYBBy6IxCDHmktgkAQqZt65wsVr99HLlWEz5+aBY4rw8Pv30MZXWdG6YKr5lpMeD/3TAWszPlo9AC02VuD4sDZXVY/dYxuNq8YY+cIoieJD6GCTmbWUpZbQtuemEfyvyRQqFhYjB4woWa4pGp1/J+ocONogUUwEfBIu0zZybHipHMXKsJmcmxcLg8uP/1L2XLrXzrWNBzWR/AdrD93/myEh+snk3CbwBCW4wY8gR2vbV5gd8uyQm5fFFVk2hbEh2mIGvzAmnmaEQzGvz470fw7ef3w+HyiCkYqfmzUIt4/9+OiMa3gmUKQfQmUpNuh8uDqb/bE/aM2TJJmtgH4I+35g2qRgCpmbn0vqeXTZQt96fvjI+4z8xo1HjvgRl4/b6peO+BGWA5L6757WfihJCO0DGqDh//+pIDlWFODSEiCwonEEOetPgYWZ2PjlHjpvHD8deDFYrNHhlJBqz4xxFxXJoUIfKhFDWptLcC/q7g8nonrvntZ9hw56SgdNn5BqdYiygQaJkyUOmt5gDiynG4PJj81G7RxDzZrAtqfuqIjMQYMRquj9JgyUTrkNjGSj6IkYTQuS+ksLOSDLhr+qiQwj5aA2xZPhM/fe8ESm0tsi5fJXKtve9ZSvQOJACJIQ+jUeNZiemrm/Vi2UtfBIm/tDgdKhvdiqIQAEbF6XDOX+8X6uCaOkwnzrL1cD4s/8dRmWiMj2FgNbd75ekYFTbedQ1mZSQO+PRvbzYHEFeG28Pihuf3i40NbtaLc/XhR3VyrSa898AMVNQ7UW13YcZVCQN+fw2X9EQD8qwmFFY1IS/V1GVvzN5EaaZxSW0L/ufDr0M+p5UDTlU1BdU5CxYvUjbdOQk35A6n3/EAhbYaQaD9IC5QEiDysiwGxWYO6SnOqNeKMzjHDTciMHGSazVh5+p8aDXyRwSp6OF8mPZ73rPr2OML/DNhv4UF44YPipOp0uxhov8ROn2FCxOBwP0U4O2KdqyYgV/fPE52/9O3T4BOy2BsignXj00eFPtruDAaNbatzMeedXOxbUV+RImhcGcaRwekeR/dXoRo//bPSzXjkzWzMTohOMqnZdQR9XmJrkFbjiDQPvopEF0U/xOJ0qiRa5Wbao6K14tTCAD+qtnh4VO3LR5v0NSANfMzYdRp8Z9H5yFK4eQK8GPkDp6pF7tdB9OJVJhQAAyO5oDBQqhOXw/nQ7IxSryt1ajw7gPTMSEtHjMzEmXLatQd14n1NNJaxUhAqT4wEpD+5nKtJmQkKUcnWxXSvK2cD3+5Z4o4o/mbS8H7yEj6DQ9oBs/ZhSCuEOlYpLxUM1Zfn4Gf/IO3eDhV3YyXv3cNRvnTOz6fDw8E2D9EqVWo8KfNzilEt3789yOi11ZHtVW///g0Zo4ZfCm0jmYPE/1HWnwMcqwmnFIYWSi1QvJwPtyy6SA+W3cdRsbpkWUxoMTW0udpT6lhO3XHd0zgb674UjMWbTgQtFymxYhSW7DASzHzPotfnQtuQMtJiaXRiwMcOgIThB/hYMmncmZCHfDrWP7GUTz01lGMTohBdJQG5yTGzhoV0ObtvGBeSMcIV+X6KA0A3h5GoNTmwJKNBWC5cOcODBwiNVIylGE0anx3ysiwlq1ocKHM5sCyl79Aia0F2RYj3l0+o0+3p9SwnbrjO0f6m7OFMLf/6cIsJBmigu9/9wS+/fx+/HL7KfG+aEaFnatnYceqWfQ7HuDQ1iMICdIRSs9JRk4JlNhacPML+2E160QRN2KYDv99Q3bQskLtzMcPzRLrC8ePMCM90SAKzWOPL8CedXPx4apZyE5uv5outpG1QqTAcl6crXUMSkEuMCltWNjLVttd4oVMsc2BKnvXJuZcKdPS48ULp8HSHd9XzLgqAdGM/LQfzaix/I2jqG1pC1r+VHVzUGRwx8pZyEk1k/gbBNAWJPoNlvOitKYZpTXNEXdyrWxwokhhJBIAlNY5sb+0Dm/efy2yk424cNktm08KAM8tG4+37r8WGrUKjEaNt348Da/fNxVbfjIdjEYtCk2dlhGvzp//7iRk+VMqVCMXGbCcF0s27Me89XuxZMP+iNtPe4K6ZifeP3IBWYnBY9U2fHcirOb2+3OtJsy4KqFfazl1WkZskqL0b9fQaRkceWw+0v0NHekJMYoj7UbGKY/Yy7IYKe07iFD5fL5O81ZNTU0wm82w2+0wmUydLU4QIkqjzATh99Dbx0SrgTyrCdtWRk4HnZJ9QiBS2xclojXt45MEW5fsZCN2rMyHTsuInngWoxa3vXgIxTYH8qwmPHPHRJoNHCGcumiX1UztXM1HPwYLdc1OTHnqc8XHdIwKx5/4FhiNGuV1/O9U2C/Jz3FgI2w/q1mH21/+QmaELxyrAslMMuDD1bNIcEc4XdFrtCWJXiOwWPvLR+ehuqkVa985HjRcvbCqqcdnhl4JQj1gmc2BpZsLgvyvAOBco1s0fA6cnwlANjtTOKAW1/D1fTtW5WPZy1/g5EW7bLB8YVWTGDXsDnRi7llOXrgcdHswCcAXdp8J+ZhbMtM2MzlW9pi0VIIYeEi337YVM1Fe1wLO60O13YX7/vqVbFkVgO0rZlLadxBCW5PoNQ6drZcVa1//9L+x8Nl9QeIPAPJSI29+JqNRY2yKCcceX4jX7p2CTXdOClrGw/nw21tykGzSdvha0rqbYpsDh8sbxOiiNAWTnWzs8vcgWGI4XJ6gsXLElXHz+BTRz1Hlvz1YYDkvCs7Uh3w8JyWyJjwMhVrM/kCYdTw2xYTZmUliel/AByBWH0XibxBCEUCi10gx62W3lYqMM5NisOGuyciwRG5nqE7L4PqxyWA5L3JTYoNqA6UdcoFEqfkr7OHmaMz4w+do43hH/clpw0TLmfb0cCx2rOzadAxplFUaSRSMlilKc2UY9VoU/mohdhZewqK84TDqOxb6A4nyuhacUZhqkxCjwXevHYXvzxiJfxfb4GG9iNKoMTuz/6bRsJwXSzcfRKHfomkbTZHpFYTMR3ldCx7ZcgKFF+1UjzyIIQFI9BoZFiPShkWj8nKr4uOv3TsFszOTBsyBnNGosX3VLJTZHFj91tGgUUlKtHmBI5WX8Y8vKiGU1bjavDjf6MKW5dNxuLwBk9OGwebwdCttK7XEaGW9SE+IQXm9kw7aPYhRr8Ud16b192r0KCznxSPvHFd8rN7JYdO/z2LTv8/K7tdHqXHs8YX9IgLL61rEOrXCi3aU17UEpaWJnkGICG4jz85BDwlAokeR1qAB/MkTCgIw02JEilmP8roWjIzTo8ruHhAHGiEt/PGaObIB67lWEzwshxIFUfirD4Lnbq564whidFr+CvsK5uIKlhhCneWHq/K7LSZ7C6VGoMHKQPmslQ1OFCqUYnSEq82Lw+UNmJtt6aW1Co3DLZ/44fIENykQPQvVeQ5+IvcIRQwoWM4rE0TCxIuvQ1iptHracOPz+wG0d51diRDqa8Sr5JX5MsFbUFaHe177stPnn6l3AeCNpK8kXStYYkhFRySlKTua2tCTDSuR0Pzi9rCY+Jt/wc36xA7aSBWBwogwaRNShsWAP902Abe+eDBojCHARwD70nNP2KYmnRr3/OWw7LFTVXaMHzmsz9aFIAYjkXl0IgYUSpYpJy/asePIBcXlo9SQpYWFFOZArFsLvErOz0hEXqpZZqsAAFcPN+JsnVOs0ctJMUKt1vRIjY0wNzgSUZraMDfbIttnpNY43UH6Wv15EXGgrA5u/0xVN+vDgbI6LBg3vM/XIxwYjRpblk/Hkk0FKK5xINtixI5V/DYQah7nX52I4+eb+qUGsDMbpuvHJvTJehDEYIYEIHHFVDY4FQ/UG/edVVga2P/zufjx68fEFJQYARwEdWuMRi3aKnhYLx5+h/c6ZNRqHHlsPr4814gUs140U+3vqFVvE5iiFiJI0n2muMaBxRsO4J8Pz+nW9yB9rYF4EdFfVNndKK7hpzwIEz2uSjLKah4XjOuf32OoY4rA56frcec02sYEcSWQACSumPgYBlEaFdq4zmfhAoDTA2xbmS+ayw6kGsBwENLDZ2sdYqNIYVUTbA4PZmcmiSPehkKNTWCKmtGocbbWAYtRK3ooAkBJbUu3C/ul6cz+vIiYlZEIfZQarjYv9FFqzMpI7Jf1CJdI+d6UkK6bEjnWwePFSBD9BU0CIa6Iyy1uTP3dnrDFX3ayETsfmj0ohF5nyFKTI8zY8pPpovnzQKp37CkC075C9Elg19o53e7sDGw+Cpxc0VcMlCYQgUionQyFsG7xMQw+OF6Flz4vQZWDLydIMGix86EZGG4e3BdQBNFVuqLXSAASXUI6Qqi8rgWLNx6AwpAMRbIsRnywqvu1XgMR6Qm2ssGJeev3io/tWTd30EcApZytdcg+f2ZSDEpr+WhoRlIMPlrNXxgI+9f5Rhc4rw8atSpklFhoPuK8PnBeH6ouu/DcZyU45W8+irQRg0T3Ka1pxsJn98nu++qx65EYGzmRS4Lob2gUHNFjSAXMZacbNz63H3UtLBgVwHZy6TAu2YA2L1Ba24KsJMOQE3+APM0bySm3vkD6+XOtJrgl80bLap24acN+RGvUOHXJAR2jEhsqAOVOcak5cCgKq5qwv7QWI+Ji+jwaGMnRtYGC9DtMTzQg0aBFXYtHfHzz5+V4YnFOP64hQQxcKAJIhESasstK0qOk1nVFrzfUIl5KDHVRIETsVrxxJCwjbSWE/SgwoqhElApo8x/hRsfp8dFDs/rEJidSOpMHMkrfYZ3Diem/b9/meVYjnr1zIjKSqCaQIICu6TU6IhEhkXbihRJ/WclG/OWeKTj++Hz85Z4p+GjVTORZ+Z0uL9WEPP9cyaEY8VJCiAhGshjozZmrjEYNjVrVZfGnj9IAkO9HafExyLIEX1Doo9T4YGU+RsXHiOIPACoaXZj81G64PWzQc3oapc5komsofYfDzUZ89dj1uG0iP5O5sMqBBesP4Pj50DONCYJQZmjl44gukRYfozj7VmBMgh4fSPzb5l2tA4Agc+ShHPEaaPTFzNW0+BiZV2KUBuKYPKGLNtdqwtO3TwCAkDWAjEaND1blY/GmApTUOJCRFIOf33g1ZmcmosruxjkF0dXKdn2aRaiobUfR3KGe7u8JQn2HibExKK+Tb9tbNn2B44/PxzCDrj9WlSAGJJQCJjrk89M1uO+vXwXd//b90zDFb+tBDB4CC+13rZ2D9ERDlwVQZwipYIC3ATrf6BL/31VLIKX1YDkvvv38PsVIY9GvFoadBg6Vyg0nxTvU0/09QajvsKzWjgXrD8iW/enCLKyan9nXq0gQEQWlgIkeY8ZVCWL6TQ3gsW+PRdGvFmJ6RiKd1AYhnFd+PejycLh180HMW78Xt24+CJbzguW8KK1pxtJNBbL7u4LglZiZHAudlpH9v6spcqW0OqNR48NVszAmyRC0/LmG8GtZldKQLOdFQVldpynegZDuj3RCfYcZSWa8/+A02X13Tx/Zl6tGEAMeSgETHaI0a5YYOqx+66gomE5etKP4UjN+sbUwaOzflU7f6I1omU7L4N4Zo/H4B6dk9x8914ic1PCaBgLTkBajFv/1/H6U2BzQagAPx4/547w+UQT35OegKKIyLOfFrz84DQBINkVj5+qZlP4liC5CZ3OiUyJ51izRu5xrcCEjMQZl/pqrW18sQGuA/0+oGrdwxQvLeXHLpgIUVTUh12rC9h7y7WM5L978z7mg+y2m6LBfg9GosXXFTNGbcPGmApTYeANrj79u8ZtLDix8dh8yk2IQHcWgqKpJTAsD3ReEfVGPOVCRRmZrmlrR5PYisXse4gQxZCEBSBBDFJbz4uT5y/jwRBWWXzcaTk9wChiAzOhbKv5yUmKx7lvZmDkmQbH+LVwblDKbA0X+udBFVU0oszkwNuXKa40rG5z45pJ82ogawLT0uC69jpCGPFvrQEnA9BIpgqk1wEdFy+tasG7LCZy8aEd6Qgw+XJXfJQua8roWsVGm8KIdxysvY4p/lvJQh5psCOLKIQFIEEMQlvPi5hf24ZsavknitUN8pEyo95RyrsEJFYBAaejhvPjh618h12rCs3dMlBktK9XOhUoRV9tdQbd7QgBazTqxq1jAC2DGH/6NY48v6LCcgeW8KLM5cK6+BRq1CrMyEoO6lzti/Ag+xSx8B+X1Tkz67Wf48tF5aHCy3YoIfuflQzjw8zkYEUehLmlkltLjBNE9SAASxBCkssEpij8pLsl0DoFoRo1WVt7kkZ4QI3bYFlU1YeGz+2RpT87rQ57VhMKqpk4jNHyjES/U9FFqzLgq4Uo+mkiV3S0TfwKuNq5DKxhpSlogmlHjhe9OxN9/OAXfefkLlIXwMWRUwPaVMzE2hReA6QkxKK/nI4NtnE+cmx2OOXR6ogFxOg0a3e3bZNYf9+H0b26gWlzIp+wQBNF16LKJIIYgafExyE7Sh7VsK+tFtt9wOdcaiyyLURQ1UoS0562bD/JWMioVdq2dg60Pdix0+EajhXj9vqk49vjCHhM3afExyLUGRxL1URpM6yCVWtnglIk/gP8Olv/jKK793e6Q4g/gxyOue/ekeHvDnZMQJfnobRwfRw3HHJrRqLFi7pig+w+XNyi/dy8aeBMEMfigy0iCGIIwGjX+e1GOosdjIJkWI95/cAZsDg84r0/mEwi0mzcHpj0LL9qhUavCSs/1VqNR4NSP396Sg+9cM6JDkSkIx0ARCLQ3fnREcY0D+0pq8exnpSi8aEeO1YQWVxsqGl3tM43DrFu7c/ooPPVpiXhbBWBy2jDZMiznxelqO1a9eQwVDS6kx8fgw9VdqzckCGLoQQKQIIYoU0fFQatRweOPSkVrgDGWWHwdMPml1ObAna8cxraV+WA5L3SMGm5/SjjTYsQ2vzhMi4+B28OKKWN9lAZWc99bcwjdx5zXJ3YvC1jNeui0jMyIWlq7CPDiePvKfJTZHCivbcHvPv4a5xvdAAAdowIAuNmO/fN/+Hq7sD5V1YRXfzAZADDcFI3oKCboPUPBaNRgAAgy1gfA5vCI4k4pXV3e4MTkp3bjxBM9F00lCGLwQUcHghiCsJwXd736H1H8AUArB9idHsXlC6uaxJSlW1IPWGpzwObw4KokI1jOi9tePCTWC7raOFTZ3X1apyXtPs61mjAmQY8z9XyTSbRGhZljEmT2KgCQZTHgg1WzZGKJ0agxNsWEsSkmLMxJRpnNgWq7S6xPPHimHn/69HRQl7ESKgD3/+2IeDsr2YgPwrS6OVBWB2kMc3S8XhY5VEpXA3zKen9pHbSMmvw7CYJQhGoACWIIIu3SFchONuKivVVx+bxUE9LiY/hOWEldXV5qeyqzssGJYlu7IIpm1L0aAVSqeZN+rqKqJlH8AUBGciwYjRqVDU5ZJ2+JrQVLNhWErJ0TxOD1Y5Oh0zLQaRnMuzoZL9x5TVjrGRgrLKlxYMnG0O8nxRuwyM+/fbVMOIaqc4zWqPDQ28dwz2tfYtKTnwWlwgmCIOiykCCGIFIftbxUM55ZNgEj4/RY9vIXOHnRDqs5GvooDc7UOZGVbMRb908TU6bvPjAD5xtdYDmvTIxYzTpZx3Ar6+21CKA0ipdlMeKDVXxETdp9HMgpfxRTyc6luMbR5WkmI+P0QTYz4VJsC+/90hLkdYLpifLRdkK6urSmGdV2N65JM+PEhSZ4WC9+/Hc+6thZ1zNBEEMTEoAEMQQJ5aO2Zfl0LN5wACW17Z2uJTUO3PrSIdEEOTclFu89OFMUi4KlSZXdLbOLyU6O7TWDXqlJconNgW89uxfRUQxKbQ7kWk1YPH44Pjh5SfYcfRQfkWQ0amxbMRNlNgceevsYSmoc3TITDmUzEw7hvl+GxSg2pOSkxEKjVgUJb0ajxtVWM6628k04c7N1cHtYseGks65ngiCGJiQACWKIouSjdr7RJRN/AJ8aLpZMwCiqbsahs/VBRs9CeriwqglZFgN2rOy70WWV/iYNgE/9KtXFudraI5JCWvefD83ulpkwy3nR2saJDTFKRtmheP2+qcjPSAy7CWT7ynyU17XgkXeOy/wWO7fWoRneBEGEhmoACYIIyag4Pd5/YAYyLXKhmGLWY3wqH3GSRbNUfJdsdBTTq+IvPdGA3JSuTcTIshiCom6CCO6q+Lt180F8+4UDYkNMuOJvTGIMpqXHd+n9GI0aGrVKTGuH4yEItFvrkPgjCEIJEoAEQYikJxpkYu9cowvbj1fJzIxzrSZkWIzYumIm9qybi60P8tM/CsrqZLNrwxEp3YXRqPH2T6bD78oSFi/ceU2PiFKlBppxww0YFUZK90ydE7e//EWXzZqFmk0g/PQxQRBER9ClIUEQIoxGjQ13TsKNz+8X7/vljlOyZe66No33A9Qyov2LYL3SVaPjK+HLc43oxI5PJC+VF609QVp8DLItRlnH85k6V9C4vFAUdjIbWQmafUsQRE9DRxGCIGRkWIzI80eblHh0exEm/maXaC0ijYi52ji8ft/UTse/9QRJxmjZ7Si1cjjwpe9dg20rwvPdCwdGo8aOVfnITuYFXHpCTNjiD5Bb53T1fbuariYIgggFHUkIgpAhdMm+8v3JsvuTjO2jxdysFwfP1AMITk9OS49HZYOzV2fSspwXv9haKN7OSDLgy8fmYXRc8Hzj0WFO3egKOi2DnQ/Nxp51c/HhqnxEaTrPRWckGfDxQ7OwrZMGDoIgiL6AjkIEQQTBaNSYnZmIjKR237lah3xKSIrE5Hn9sgnYtXYOtvxkOpa9/AXmrd+LWzcf7DURWNnglHn9vXj3ZAwz6PDJ2jkYHS8XgZoQkcErRYjIGfVaHPrv6zpdvqy2hR/tRuKPIIgIgI5ERL+iNM2B6H9YzovbX/4CZQGWMAKj4qJR2eDEl2frMPdPn2Phs/vwyJYTqKh3BtnD9AaBUUfBIFmnZfDJw3OQ5U/P5qWag8yTe4NGJxfWcpw33H7h3oV+dwRBUBMI0W9ImwfGp5rx5v3XYl9pLUouOXD3jJHiSTW9F1J4RMcEjksL5FxjK5b/46jsvsKLdqx4o33mra4XR8F11BSh0zLd9vfrDiznxdp3jis+FhfDoNHZPoatt6KRXSHwd7d1xUywnJc8AwliiEG/dKLfkDYPnLxox8QnPwPrj5A8t6dMXC43JRbbV80iEdiHpMXHINNiQKlNOQIYirN17RE/dy+OggOUjazDeaynKa9rUTSeBni/xESjF6W2lj6LRnZG4O+u+FIzbn3xIFpZL6IZNY48Nh9GvbaTVyEIYqBDZ1Si35Cm8QCI4i+QoupmcQ4t0TfwdjDXXNFrjI6PGfJ+dV9XN2Pz9yZjz7q5EdP8If3d5aWasOKNo7L5zTc+t3dIpoYpLU4MNfr/aEQMWYQ03h9vzevvVSEUyLAYkZkUHLGKN0R1+txoRo2PVvec9Uokk55oQJ7VBIA3yf5kzWzRRkeoT4wk+xbhd7dn3Vw8s2wizgXUaV6we/CvU5dEIdQVYcRyXpTWNKO0pllc3uHy4J3/VMLh8nTy7P6D5bxYuqkA89bvxS2bCmTrTxCDFZXP5+u0KrmpqQlmsxl2ux0mk6kv1osYQrg9LCb8ZldIL7UxiTH4dO3ciDmBDiXcHhY3byxAqd/0OJpRoZX1QatRwcMpHzrunTEK//3tsUOqlozlvLKaw8DbkYrbw+LqJz5VHGWXZzXh3QdmYNnLXwTVCx46W48Usx4ZlnZhy3Je3LKpQEyH51pNeHJJLpa+eBAAoAJQ+KuFEZdeZjkv/l1sw/1/OyK7n0pPiIFIV/Ta0DlCExGLTstg64MzsWjDgaDHxiQZsHM1HYT7C52WwcdrZqO8rgXn6lvEk6SH8yFKDbQpaPYvzzUOue0VWHPYlzWIV0KV3R1yjnFhVRMOlNXJ6gXLbA4s3VwAl3/DS0VSYC1kUVWTKP4Afl7yzsJLuMM/SSYSBDLLebF080HFhqei6maU2RwYm0JBD2JwMrSO0kTEkj08FrkpseJtqzkaO1bMwKcPzxlSkaRIhNGokZkci1EJ8nRwmxd47d4p2PrADCTGtG+jU1VNvToHmOg5pPWAMVFqLJlkkT3+x0+Kxf9nWQw4V98iij+gvT73bK0Dzta2Tt9vUd5wPlK48QDmrd+L+X/6vF9Tw512u9dT7TExeKEzKxERMBo1tq+aJTZ7kPVL5JGeaECu1SRGefJSTZidmYTKBifqJFYnKgDxMXRoGQgE2ukAwFkbHxHLshhRIpl3XGJrwdP/KpE9f0yiHivfOIKSTrrFU4fpsXP1TBj1WpyubkJRdTMA4NxlNyY9uQuF/3NDn1/osZwXnNcn26cDo9pP/6sY869OpmMRMSihozQRMQiRJiIyYTRqbF+ZHyTS0+JjkB4fg3J/1M8HYOnmQ9j1CNVtDgQC09Xb/ILQataJ9X8CpRJBCABQqTsUf1ZTNP78g8kYm2IW94Vqu0u2TJsXOFzegLnZFqWXuCJCpZqlXoh5VhM+fohPY6eYorFoQ4HYGFNqa8H+0lqMiIuhi1Ji0EF7M0EQYSOI9MzkWPFkyGjU+HB1PrSSebjl9U5KAw9QBEGo0zLYumImdq2dg1yrch3cmRCTYgSqmlqx9u3jsvtmXJUAXVT7qSdao8K09PgrXu9ABJE3b/1eLFj/bxRdaATLecFyXhRIahsLq5pw8oIdKaZo2Bwe7Fydj2wLL4ijGTXu++tXWPjsPtyyqYA6g4lBBXUBEwTRIzhcHty8sQDl9U6MH2HG1gcjw/eOuHJKa5qx8Nl9QfenJ+hRXu9SeIacT9bMljVTuD0s9pfWAQBmZyb2Svr3bK0D89bvld2XkxILtVqNwot2RDNqtLJeqMBHrYW/mUkGPH37eDz01jGca3R3+DkIItLoil4jAUgQRI8RKd2dRM/Ccl7Me/pzVAYIohVzrsLmfWc7ff5r907B9WOTe2v1FGE5LxY+sxfl9aEj0RZjFGyOzptXBDKSDHjx7smUDiYilq7oNdqDCYIgiA5hNGp8f/rooPuzhhsQzXR8GmHUfNq3r2E0any4Sl6aEEhXxB8AlNW2YOGz+7CU0sHEIIAEIEEQPYK05uq/ntuHb6rsdJIcJLCcF/84XBl0/8/fL0LBz+fCao4O+dwvfnF9v1k56bQMPlg1C6/+YDJWzb1KcZkR5q4bUxdWNdF4SmLAQ13ABEF0GbeHxeHyBkxLjxdP7pUNTrGwvqS2Bd9+4QCyko34YGU+eTkOcCobnEEj4wCglfPhVJUDe9Zdh5s3HEBpbQvGJMZg7cJsXHZ6cMtEa59P/nB7WBw6Ww9LrA7//f5JFFY1Ic9qgquNU1xerdZ0+HqZFiN++q0swAcsf+Nob6wyQfQLdFQmCKJLuD0sJj35GVxtHPRRGhx7fAF0WgZp8THB3nE1DizZWICda2YPqBFphBzBMPrkRTtGJ8SgqtEJjxfQR2nEi4CPH57T79tWum9KKZRMKAmksrHjJpZSmwPr/1WCrQ/OQF6qGYUX7chL5Wc8E8RAhgQgQRBd4nB5g3iCdbVxMg+3n92QjR//XT5TtdjmQEFZHSanDcNtLx5Csc0hzpUlETgwCDSMZjlvUAQ4EsbfSffNnqTE5sDNGwvw4ap82BweuoAhBgUkAAmiO7Q6gKJtQO5SIDryZ772JNPS46GP0ogRwGnp8TJjXa0G8EjOwSoA97z2pWi7AfBzZSsbnP0uGIjwkQo8RqPuFePmK2Vaejx0jAputlNziy5TXu/EbS8eEqPZBDHQob2YILpKqwP4/Qjgw1X831Z/ypNjgUtFQMm/gDZ36OdzLFBXxv8dgOi0DI49vgCv3zdVTP9K6/88AQEY4VQsiD8AyE42iqPHCKKn0GkZbLzrmi4/75XvT0ZeCLPr1GF68f/FNgcZnBODBhKABNFVirahXdb4+NscC7wyH3gpH3jzduCPo5VFIMcC/7cA2DgZeHU+cOkUYDs94MSgTstgbrZFTP8JNWIdIZhxRDNqvP/ADIqiEL3CrIzETq1ppORaY3H9WAu2rczHH2/Nkz2WnmDAp2tmiZNBxo8w04ULMWigIzBBdJXcpWiXMyr+dmMFcOl4+zKsCyj3T05ocwOlu/m/jRVA1TH+/urjwEszgc3TeDE4wESgFEajxpbl0zHCFBVyGWkk0Obw9M2KEUMOnZbBkcfmY3S8vvOFATx7xyQwGjUYjRqL8oZLf9l494FrYdRrsXPNbOxZN5em2xCDCtqTCaKrRBuBX1wAbt7I/402AnGjgcSx8uVMVl70/e9o4I1b+b+xwwHrpODXrD7Oi8MBzPlGFy40dW6sm2uNpSgK0asY9Vp8tu467Fw9SzEaaDXxvoXjR8i7eaubWqWxfdz1yn/Acl6x/pHEHzGYoL2ZILpDtBGY/P32BhANA/x4NzAsnb+dkAUkZAAVBUCb32aizQVUHgbu+wS4811g+Pj211NrAU8LHwUc4DWCnSGtBSSI3oLRqJGTasaJJxbitXunYNxw/reaa43Fnp9eFxTRc3tYFJTVyV6jxNZCNX/EoIW6gAmiJ+BY4K+LgMvl/O36EuDlucCPPgWi9Lz4i9IDadOAv9zAR/wSs9uf7/UAf54DJI3lxWDNSSBlInD/bl5cDgDSEw0YnRCDig5mrwJAqf+kSh3ARF+g0zK4fmwyZmcmyXwKpfuf28Ni4m/+FdQ9nJdqomg1MWihCCBBdEY4EbnGCl7USak7DfzlRiA+i79tHgXUnm5frq4YMI+WP6f2NC/+AH652tNXvPp9BaNR46NV+WBCj14FQClgon/oKI17uLwhSPz9fmkutq3Ip7QvMWihPZsgOqLNDbw8i+/a/b8FoUVg3Ghg+ITg+2u/AWpO8P+vOw289m354zMe7Pj93//hgEoFG/VaPHHT1R0uIxTdE0SkIPgHCuij1Fg6KZX2U2JQMzBySwTRH3As8Mr1gO0b/nbVMT7SZx4BnN0HmFOBpGw+RetlAa41+DWGjQaaLgJef3MEF9D9qiQapdQWA/VlgGVsx8tFEJNHJ8huj47ToqKR/9x5qSYaoUVEHDotg+NPfAsH/DWAszISaX41MeihPZwgQlFfBti+br+dOBZw24HN09sF3fAJwI/+BbxynXK6trm6fVkl/ra48/XwDpwIIABkD49FznAjTl1yICfFiPcfzMd5/7zV9EQDRVWIiESnZbBg3PD+Xg2C6DNIABKEEhwLbP2J/L66MuDVefL7Lp3g/f6EKGHQ6yhEBaV4w/DDUw+snymjUWPH6tmygvvM5Nj+Xi2CIAhCAl2KE4QS9WW8uJMRIhJnsgKWccqPRYVnRhuS5PG8ncwAg3zTCIIgIhs6OhOEEi11nS8D8FYtSWOBH38OWPzND0lXAz/ZC6w4DDxyGjCN6P563PbKgLGBIQiCIAYOdGYhiEDa3MDrN3eykAr4yb+B5FxeoGkYYPkBvkkkbjR/m2P5DuKmC91bj5RJAzL6RxAEQUQ+FAEkiEDK9wHobFqFD9Aa5dE5DQMkZrTf11ghrw2Mu4o3eQ7ku28DDxQAyf6O4IRs4IGDwP2fUfSPIAiC6BXo7EIQgZisoR9j9ADr4uf5xo3u+HXiRvPLVR3j08O3/h/w0kz5MpZxQOZCXuj9ZI88gkj0OyznlTWzEARBDBboLEMQgSRkgP9pSJo+Zv8/IGcJ/5j9QngiTcMAP/qsXdQBfFq3+hj/f6F2UHgdIYJIRAQs58XSzQdReNGOvFQztq2YSSKQIIhBAwlAggjEfgFBHb85twDDc/j/d0WkBYq6+z/jO4wBXkxSpC9iKa9rQeFFOwCg8KIdpy5exoS0+G69ltvD4nB5Az9xggyGCYKIAOhyliACiRvNd/cKpEzgJ370BBqGn+phGUviL8LhvPLZsEs2H0Jds7PLr3OhsRnjnvgU97z2JSb+ZhfcnoFl7E0QxOCEBCBBBKJhgPt38zYuKw4D9+8hsTbIYDkvztY6wHLKzT4s58WFxmCxN+2pz7sk4OqanZj1x31iS5Gb9eLgmfrurDJBEESPQmc1glBCiNQRgwahocNq1mHZy1/g5EU7xqeasTWgto/lvLhlUwGKqpqCXoMDcPBMPeZdnRzWe76w+0zQfSlmXbc/A0EQRE9BApAgiEENy3lRXteCte8cR1FVE7KSjSipcQAATl60o7yuBRq1Suz0La9rURR/ApbY6E7fr7LBifgYBp8UXgx6PCk26so+EEEQRA9AApAgiEGFIPiEGr6fvntCJuhKahzITIpBaa0TealmPPLOcRRWNYnRwM6IjtJ0+L6PbDkhNo8osWTTF9j7s+uoo5ggiH6FBCBBEIMGlvNi6aYCFHYQwQMAqNTYtXYOAGDhs/sA8NHAygYn0hMNyLWaRNGYk2KESqVGUVUT8lLNSE80BL3n6eomrHrjKCoaXZ2u48XLLpTXtSAzObYbn5AgCKJnIAFIEMSgobLB2bn4A1Bq41PA6YkGjE818/WAI8xiGnj7ynyU17WIywivHWgIzXJeLNl4AKeqm3vh0xAEQfQeJAAJghg0pMXHIM9qCksEPvLOcWxbmY+tK2YGiTtGow6K0F2VZAx6jfK6liDxNzIuGhcaW+ELWpon1xobFEUkCILoa6gIhSCIQQOjUWPbynzsWjsHO1bMRFqcPuSyhVVNqGxwgtGocVWSsUdq8rQaFV783hSZ+MtO5oVjVrIRn6yZje0rZ1H9H0EQ/Q5FAAmCGFQwGjVSTNFYtOErVPpr8gIG+wEA9FEaWIxanK11dHvWb3qiQYw4xuk0ePC6DIyI08nSylt+Mh1VdjfNEyYIIqJQ+Xy+UJkKkaamJpjNZtjtdphMpr5YL4IgiG7h9rCY8Jt/oZXt9NCGbIsRxTaHoh9guFxucWPSk7tlUb+vHrseTW4viT6CIPqUrug1OjIRBDGoOFzeEJb4i1KrUGxr9wOsbOj6mDcA+PSULaje779eKIDVrCPxRxBExEJHJ4IgBhXT0uOhj+r80Nbm9YkHQKEDuDssyhsOVcB9tmYPlmwqUBw119kYOoIgiL6ABCBBDDVcl4G9T/N/ByE6LYMvH52PkXGdj1zzAlAB+Nt9U7odrTPqtTj2+HwMD5jwUVzjCIoqspwXt24+iHnr9+LWzQdJBBIE0W+QACSIoYTrMvDHUcDnT/J/B6kIrG5qxflGd1jL+gB8XFRzRe83zKDDgf9egE/WzEaWv+t3/AgzLEYtPj9dg9PVTeKIuJP+KSHCGDqCIIj+gLqACWIo8Z9Xg2/P/Wn/rEsEMS7lypvbGI0aY1NM+OdDs8VZwJOf2o1Wlo/yZSYZsG3FTNmUkUe2nMC7y6fjfKMLzlYWJy/aMWnkMGRYjAOmc1gQtgNhXQmCaIe6gAliKCFEAAV+fg7QD+uvtek1WM6LWzYeQFF1MxgV0FlPSG5KLLav6jl/PpbzYuEze1FeL08Bj4qPQZQaKKtrvz/TYhQnkwhoVADn4x/7cFU+dNr+vVYX5hwD8skoVrMOt790CIVVTcizmrBtZT6JQILoR7qi1ygCSBBDCf0wXvT951Xg2vsHpfgTUKl5IZJpMeCbmo5TrUXVzT06n7eywRkk/gDgnEKncaD4A3jxJzx288YD+HjNnH4TVoHj7jItRsDnRWmtE1kWA0ps/HdbWNWEMpsDWkZN0UCCGADQL5Qghhr6YXzadxCLv8oGJwr9tXbf1LRghCmqk2f0LGnxMeIEkCul1NbSbYua7uD2sNhbbIPbw1tnl9Y0y8bdldocKK3l10cQfwIr3/gK89bvxdIQHdAEQUQOJAAJYrDS5gZKd/N/A+FYoK6M/zsISYuPwfhUMwAgOzkWF5raOlw+L9Xco/N5GY0aO1bmI9vCi8BAm5iukGExdNuipqu4PSwmPfkZ7nntS0x68jOcrm7CBf80lXA4U8cvW1jVRA0uBBHhUAqYIAYjbW7gf0cDbS4gSg/8vwogym+LwrHA/y0Aqo4B1knAjz4DNIPrUMBo1Ni6YmZQnVogmUkGbL57MtITDT2estRpGexcwzeEuDwcFm040K3X2XjnNX2WTj1c3gBXGwcAcLVxuPH5/ci1mhCtUaGV69xcWwpFAAkisqEIIEEMRioKePEH8H8rCtofa6zgxR/A/22s6Ou16xMYjRpXJRmh0zLYtjIfu9bOwccPzUJuCl/nl2Ux4MPVs5CZHNtrAktYh+zhsWJEMpoJ/73yUk3IsPRMKjkceBNtjey+oqomvP/gDFhN2i69lkp1JXFPgiB6m8F12U8QBM/ofD7yJ0QAR+fzkb/GCsA8go/8CRHAuNH9vba9DqNRiw0e21fN6nPbEmlE0mLU4sbn9uOCvWOfwtfvm4r8jMQ+baZgNGpclaDHqUvtjSl5qSa0cUBVk6dLrxWGwQRBEP0ICUCCGGwIQu++TwFHDTDyWqB0F7DrCaDxLGAZB/xoF9B8iRd/gyz92xlCVK4/3/eTh2dj4pO7wAZkSTMtBpTaWjB+hLnPxR8AlNkcMvEHALdOSsWylw/J7tNqVPB0khJu62LKmCCIvoV8AAliMMGxwCvzgEsn+NvJ44G6YoBrlS8Xmwrc+SaQnDvkBGCk4HB5sGjDAZxr4FP1eakmvLt8Rr8ZQPMNILvgauuZ2r3RCTH47JG5ZAdDEH0I+QASxFCl9nS7+AOAmpPKyzVfBP48F0ieANz2ZyAhg4RgH2PUa7F73XUyg+X+ik4CQgNIzzVuVNQ7Udng7LfPQxBEx9ClGUEMFjgWePeHXXtOzQlg8zTg1fmD1hImkhFqE3uzESVcpqXHQ9eFBpVAXr77Gowb3i728lLNfWZfQxBE16FLfoIYLNSXAfXF8vvMYwD7mc6fW32cf75lbK+sGhH56LQM3ntgBm7a2N4xnpFkQFlt535+Vw83Yv7VyZh/dXJQRJMgiMiEfp0EMZgZObm/14AYILCcF7/YViTezrWa8N4D0/HbJTnQMe2WLtL/CzyzbCIYjTqiIpoEQXQM/UIJYrCQkME3dUi54Une6gUAGF3o5yaO5Z9PDFmk4/MAwN3GYcYfPscvd5yCm23vFXSzPjy5OEf2XPL8I4iBB3UBE8RggmN5f7+T7wHX/QwwJMr9/0o/A7Z8T/6cxGxg+b72SSHhvk9jxZC0kRmssJwXi17Yj+IaR4fLpZi0iNJoUCkZEZeTEosdq2ZR1I8g+pmu6DX6tRLEYELDACOnAov+yIs/4b7EDF7gJSpE+Zb9rXPxx7GA7TT/r83NW81snMz/peaRQUHg/OJcqwn6qOBTRHWTRyb+AOBUdTPN/iWIAQZduhPEUCIhA0iZyDd9APz/Q6V+ORaoOQVc+Ao48td2SxnzaMBewf//0gneemZ4rvJrEAMK6fzitPgYsJwXh87W4+lPi3Gqurm/V48giB6EBCBBDCU0DHD/br7jFwjt/xdoKC1FEH8CTVUkAAcRUi9CRqPG9WOTMeOqBNzw3D7RtFpguJHBJQeLvFQT0hMN/bG6BEF0ExKABDHU0DCd2700ViiLv0Ci9ED6nB5ZLSJyqbK7g8RfTkos3n9wZr9NLiEI4sogAUgQRDscC9QWA42VQEIWUF+ivNywdODG3wFj5nWteYQYkKTFx2B8qhknL9qRlWzEC9+dhAyLsV8nlxAEcWWQACQIgodj+YkgQn0gNKGXvVwO7Pk9kPmtvlgzop9hNGpsXTFTrA2kaB9BDHzoV0wQBE9jhUT8AQDX8fK2k8A3O6kLeIggRPtI/BHE4IB+yQRB8JhHAIy+a8957wfA/y0gEUgQBDHAIAFIEASP/QLAujpfLpCqY3z0kCAIghgwkAAkCIInbnT72LjE7M6XV0fzf62T+OcSBEEQAwZqAiEIgkfDAD/6rH1s3P99q90KhtEDt70GXDWbjxQCvOizX6BxcARBEAMQigASBNGOdGzcrX9uv591AUmZQLSR9xC0jG0fLUfijyAIYsBBApAgCGUSMtpTwpTmJQiCGFTQpTtBEMpIU8KU5iUIghhU0BGdIIjQCClhgiAIYlBBKWCCIAiCIIghBglAgiAIgiCIIQYJQIIgCIIgiCEGCUCCIAiCIIghBglAgiAIgiCIIQYJQIIgCIIgiCEGCUCCIAiCIIghBglAgiAIgiCIIQYJQIIgCIIgiCEGCUCCIAiCIIghBglAgiAIgiCIIQYJQIIgCIIgiCEGCUCCIAiCIIghBglAgiAIgiCIIQYJQIIghixuD4u9xTa4PWx/rwpBEESfwvT3ChAEQfQHbg+LSU9+BlcbB32UBsceXwCdlg6JBEEMDSgCSBDEkORweQNcbRwAwNXG4XB5Qz+vEUEQRN9BApAgiCHJtPR46KM0AAB9lAbT0uP7eY0IgiD6Dsp3EAQxJNFpGRx7fAEOlzdgWno8pX8JghhS0BGPIIghi07LYG62pb9XgyAIos+hFDBBEIMO6u4lCILoGIoAEgQxqOC7e3fB1eaFPkqNY48vpPQuQRBEAHRUJAhiUOBwefDBiWo0Oz1wtXkBAK42L944XIk5WUlITzSA0VDSgyAIAgBUPp/P19lCTU1NMJvNsNvtMJlMfbFeBEEQYeNweZD3613o6GCWl2rGthUzAQCVDU6kxceQICQIYlDRFb1GEUCCIAY8H56s7lD8AUDhRTvK61qwbssJnLxox/hUM7aumEkikCCIIQkd+QiCGPDkWM2dLpOXagbn9eHkRTsA4ORFOyobnL29agRBEBEJRQAJghjQsJwXj24rDPn4K9+fjNGJBoyM0+P2l78Q789LNSEtPkZ8DSEtDFCKmCCIwQ8JQIIgBjSVDU4UVTUpPpaXasL1Yy1gNGqcrXWg0B/9A4Bf3zwOL/37DL57bSp++NejOHnRjrxUM+DzobCqCenxMfhwdT6Mem1ffRSCIIg+g5pACIIY0LCcF0s3HxTFXa41Fn/6zgQwGrWs85flvLh180GcvGjHmMQYnKnrPP0bpVHh2C8XkAgkCGJA0BW9RgKQIIgBD8t5UV7XAgBBou90dRNOXrBj8YQUMBo1Fm88gBJbi+z5jApgfXydoL3FjcrLreJjo+JjsHvdXEoHEwQR8VAXMEEQQwpGo0ZmcqzsPpbzYsnGAzhV3QwAeGx7ET5aPStI/AG8+Hvt3imYOioO//XCftlj5xqcOF3dhNwRw3pt/QmCIPoauqQlCGJQUtngFMUfAPgAHD3XiMykGMXl//TJady0sQCVje6gx1a9dQws5+2tVSUIguhzSAASBDEosZp10Grk971+qByltcq1f19fcqCiXvmxinonCsrqSAQSBDFoIAFIEMSgpMruhodrv50Wp0NZCPEXiArAgZ/PwegEPlqoj1Ljnte+xNLNB0kEEgQxKCABSBDEoCQtPgbjU3mD6OxkI/750GzkWsNrYvMBOHWhGbHRfJm0MFtYmCZCEAQx0KEmEIIgBiWMRo2tK2bKTJ0fXpCJ+/92JKznP/nhKVxo8gTd72EpAkgQxMCHIoAEQQxaGI0aVyUZwWjUYDkvnvusNOznKok/ADh54TKlgQmCGPCQACQIYkjQ0cQQgVAHRMb/gArAL7YVUS0gQRADHhKABEEMCaQ1gaHwgp/+AfCNHwAv+lgvLwIF13yqBSQIYqBDApAgiCGBUBO4a+2cDptB2jhe5gmNH4LoCyz9c7jZ3lhNgiCIPoEEIEEQQwZhYsj2lfl45fuTr+i17vjzIbg9JAIJghiYkAAkCGLIwWjUuH6sRRYJZPjML3KtJnyyZjbyOrGM8XA+HC5v6M3VJAiC6DXIBoYgiCEJo1Fj+8p8sZZvZJweVXa3aBmzzf/Yj/5yGJX21qDnR2lUmDAiPF9BgiCISIMigARBDFmElHBmcix0Wka0jJE+9s+H54hdwFLaOB9m/OHflAYmCGJAQgKQIAiiA4x6Lb56bL7YHSwVg642jtLABEEMSCgFTBAE0QnDDDoU/upbOFzegAkjTJjxh3/D1cZBH6XBtPT4/l49giCILqPy+Xy+zhZqamqC2WyG3W6HyUQ1LwRBDG3cHhaHyxswLT0eOi1dRxMEERl0Ra/RkYsgCKKL6LQM5mZb+ns1CIIgug3VABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMUgAEgRBEARBDDFIABIEQRAEQQwxSAASBEEQBEEMMZhwFvL5fACApqamXl0ZgiAIgiAIonsIOk3QbR0RlgBsbm4GAIwcOfIKVosgCIIgCILobZqbm2E2mztcRuULQyZ6vV5UVVUhNjYWKpWqx1aQIAiCIAiC6Bl8Ph+am5thtVqhVndc5ReWACQIgiAIgiAGD9QEQhAEQRAEMcQgAUgQBEEQBDHEIAFIEARBEAQxxCABSBAEQRAEMcQgAUgQBEEQBDHEIAFIEARBEAQxxCABSBAEQRAEMcT4/zAi/4SziuL1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.scatter(z_transform[:z_incart_size, 0], z_transform[:z_incart_size, 1], s=2, label='INCART')\n",
    "plt.scatter(z_transform[z_incart_size:, 0], z_transform[z_incart_size:, 1], s=2, label='MIT-BIH')\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8beb4d65-23ca-440a-8196-b6014672595a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pkl.load(open('models/checkpoint.pkl', 'rb')).module_.to(DEVICE)\n",
    "\n",
    "training_dataset = torch.utils.data.TensorDataset(\n",
    "    torch.from_numpy(X_cwt_train),\n",
    "    torch.from_numpy(rr_train),\n",
    "    torch.from_numpy(X_feat_train[:, :5]),\n",
    "    torch.from_numpy(y_train)\n",
    ")\n",
    "training_dataloader = torch.utils.data.DataLoader(training_dataset, batch_size=1024, shuffle=False)\n",
    "\n",
    "z_train = []\n",
    "y_train = []\n",
    "\n",
    "for batch in iter(training_dataloader):\n",
    "    X_cwt, rr, X_feat, y = batch\n",
    "    X_cwt = X_cwt.to(DEVICE)\n",
    "    rr = rr.to(DEVICE)\n",
    "    X_feat = X_feat.to(DEVICE)\n",
    "\n",
    "    z_beat = model.beat_head(torch.unsqueeze(X_cwt, 1))\n",
    "    z_feat = model.feature_head(torch.cat((rr, X_feat), dim=1))\n",
    "    z = model.linear_blocks[0](torch.cat((z_beat, z_feat), dim=1)).detach().cpu().numpy()\n",
    "    z_train.append(z)\n",
    "    y_train.append(y)\n",
    "\n",
    "y_train = np.concatenate(y_train)\n",
    "z_train = np.concatenate(z_train)\n",
    "z_scaler = RobustScaler()\n",
    "z_train = z_scaler.fit_transform(z_train)\n",
    "\n",
    "testing_dataset = torch.utils.data.TensorDataset(\n",
    "    torch.from_numpy(X_cwt_test),\n",
    "    torch.from_numpy(rr_test),\n",
    "    torch.from_numpy(X_feat_test[:, :5]),\n",
    "    torch.from_numpy(y_test)\n",
    ")\n",
    "testing_dataloader = torch.utils.data.DataLoader(testing_dataset, batch_size=1024, shuffle=False)\n",
    "\n",
    "z_test = []\n",
    "y_test = []\n",
    "\n",
    "for batch in iter(testing_dataloader):\n",
    "    X_cwt, rr, X_feat, y = batch\n",
    "    X_cwt = X_cwt.to(DEVICE)\n",
    "    rr = rr.to(DEVICE)\n",
    "    X_feat = X_feat.to(DEVICE)\n",
    "\n",
    "    z_beat = model.beat_head(torch.unsqueeze(X_cwt, 1))\n",
    "    z_feat = model.feature_head(torch.cat((rr, X_feat), dim=1))\n",
    "    z = model.linear_blocks[0](torch.cat((z_beat, z_feat), dim=1)).detach().cpu().numpy()\n",
    "    z_test.append(z)\n",
    "    y_test.append(y)\n",
    "\n",
    "y_test = np.concatenate(y_test)\n",
    "z_test = np.concatenate(z_test)\n",
    "z_test = z_scaler.transform(z_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7f3dbbde-e5cd-4c85-b49c-763ab902f854",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n",
    "z_train_resampled, y_train_resampled = smote.fit_resample(z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "60bfef4e-91e0-41e4-b952-22a25e451207",
   "metadata": {},
   "outputs": [],
   "source": [
    "adasyn = ADASYN()\n",
    "z_train_resampled, y_train_resampled = adasyn.fit_resample(z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "aad1af83-8449-4cf9-9ba5-a0c661ebc454",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels, counts = np.unique(y_train, return_counts=True)\n",
    "max_label = labels[counts.argmax()]\n",
    "max_count = counts.max()\n",
    "z_train_resampled = z_train.copy()\n",
    "y_train_resampled = y_train.copy()\n",
    "for i, l in enumerate(labels):\n",
    "    generated_samples = []\n",
    "    if l != max_label:\n",
    "        to_be_generated = max_count - counts[i]\n",
    "        nearest_neighbors = sklearn.neighbors.NearestNeighbors(n_jobs=-1)\n",
    "        nearest_neighbors.fit(z_train[y_train == l])\n",
    "        neighbors = z_train[y_train == l][nearest_neighbors.kneighbors(z_train[y_train == l], return_distance=False)]\n",
    "        for _ in range(to_be_generated):\n",
    "            random_sample_idx = np.random.randint(len(neighbors))\n",
    "            random_sample = z_train[y_train == l][random_sample_idx]\n",
    "            random_neighbor = neighbors[random_sample_idx][np.random.randint(nearest_neighbors.n_neighbors)]\n",
    "            diff = random_neighbor - random_sample\n",
    "            new_sample = random_sample + (.5 + np.random.rand())* diff\n",
    "            generated_samples.append(new_sample)\n",
    "        generated_samples = np.asarray(generated_samples)\n",
    "        generated_labels = np.ones((to_be_generated,)) * l\n",
    "        z_train_resampled = np.concatenate([z_train_resampled, generated_samples])\n",
    "        y_train_resampled = np.concatenate([y_train_resampled, generated_labels])\n",
    "y_train_resampled = y_train_resampled.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0966762a-86f4-407b-af3f-271c241f87e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels, counts = np.unique(y_train, return_counts=True)\n",
    "max_label = labels[counts.argmax()]\n",
    "max_count = counts.max()\n",
    "z_train_resampled = z_train.copy()\n",
    "y_train_resampled = y_train.copy()\n",
    "for i, l in enumerate(labels):\n",
    "    generated_samples = []\n",
    "    to_be_generated = max_count\n",
    "    nearest_neighbors = sklearn.neighbors.NearestNeighbors(n_jobs=-1)\n",
    "    nearest_neighbors.fit(z_train[y_train == l])\n",
    "    neighbors = z_train[y_train == l][nearest_neighbors.kneighbors(z_train[y_train == l], return_distance=False)]\n",
    "    for _ in range(to_be_generated):\n",
    "        random_sample_idx = np.random.randint(len(neighbors))\n",
    "        random_sample = z_train[y_train == l][random_sample_idx]\n",
    "        random_neighbor = neighbors[random_sample_idx][np.random.randint(nearest_neighbors.n_neighbors)]\n",
    "        diff = random_neighbor - random_sample\n",
    "        new_sample = random_sample + (.5 + np.random.rand())* diff\n",
    "        generated_samples.append(new_sample)\n",
    "    generated_samples = np.asarray(generated_samples)\n",
    "    generated_labels = np.ones((to_be_generated,)) * l\n",
    "    z_train_resampled = np.concatenate([z_train_resampled, generated_samples])\n",
    "    y_train_resampled = np.concatenate([y_train_resampled, generated_labels])\n",
    "y_train_resampled = y_train_resampled.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2d50ea2-8c99-4848-925d-e9a03be330dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(y_train_resampled, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec6b7e36-f6a9-4ad4-ace2-3f68e78fde0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "z, _, y, _ = train_test_split(z_train_resampled, y_train_resampled, stratify=y_train_resampled, train_size=.1)\n",
    "\n",
    "tsne = TSNE(n_jobs=-1)\n",
    "z_transform = tsne.fit_transform(z)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "for l in np.unique(y):\n",
    "    plt.scatter(z_transform[y == l, 0], z_transform[y == l, 1], s=2, label=num_to_label[l])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d9adc04f-0a0d-49a7-b440-590f17d66ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseNN(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(DenseNN, self).__init__()\n",
    "        self.layers = torch.nn.Sequential(\n",
    "            torch.nn.Linear(in_features=256, out_features=512),\n",
    "            torch.nn.LeakyReLU(negative_slope=.2),\n",
    "            torch.nn.Dropout(.5),\n",
    "            torch.nn.Linear(in_features=512, out_features=512),\n",
    "            torch.nn.LeakyReLU(negative_slope=.2),\n",
    "            torch.nn.Dropout(.5),\n",
    "            torch.nn.Linear(in_features=512, out_features=64),\n",
    "            torch.nn.LeakyReLU(negative_slope=.2),\n",
    "            torch.nn.Dropout(.5),\n",
    "            torch.nn.Linear(in_features=64, out_features=3)\n",
    "        )\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.layers(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "66f9909e-8d37-450b-9d75-e8115e5c9e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  epoch    train_loss    valid_acc    valid_loss    cp      lr     dur\n",
      "-------  ------------  -----------  ------------  ----  ------  ------\n",
      "      1        \u001b[36m0.0519\u001b[0m       \u001b[32m0.9173\u001b[0m        \u001b[35m0.0984\u001b[0m     +  0.0010  2.8640\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2        \u001b[36m0.0193\u001b[0m       0.9163        0.1055        0.0010  3.1282\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3        \u001b[36m0.0159\u001b[0m       0.9134        0.1136        0.0010  2.7517\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      4        \u001b[36m0.0139\u001b[0m       0.9116        0.1082        0.0010  2.7354\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      5        \u001b[36m0.0133\u001b[0m       0.9164        0.1201        0.0010  2.7454\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6        \u001b[36m0.0112\u001b[0m       0.9019        0.1472        0.0010  2.7435\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      7        0.0118       0.9026        0.1277        0.0010  2.7585\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      8        0.0113       \u001b[32m0.9201\u001b[0m        0.1096     +  0.0010  2.7580\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      9        \u001b[36m0.0111\u001b[0m       0.8990        0.1363        0.0010  2.7512\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     10        \u001b[36m0.0101\u001b[0m       0.9112        0.1080        0.0010  2.7891\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     11        \u001b[36m0.0075\u001b[0m       0.9101        0.1212        0.0005  2.7590\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     12        \u001b[36m0.0070\u001b[0m       0.9095        0.1211        0.0005  3.2642\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     13        0.0071       0.9176        0.1005        0.0005  2.8297\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     14        \u001b[36m0.0065\u001b[0m       0.9090        0.1319        0.0005  2.7509\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     15        0.0070       0.9185        0.1170        0.0005  2.7393\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     16        0.0068       \u001b[32m0.9214\u001b[0m        0.1190     +  0.0005  2.7659\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     17        0.0067       0.9128        0.1113        0.0005  2.7660\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     18        0.0068       0.9148        0.1199        0.0005  2.8213\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/182 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     19        0.0067       0.9157        0.1065        0.0005  2.8758\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     20        \u001b[36m0.0062\u001b[0m       0.9037        0.1310        0.0005  2.8501\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     21        \u001b[36m0.0052\u001b[0m       0.9143        0.1120        0.0003  2.7360\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     22        \u001b[36m0.0049\u001b[0m       0.9085        0.1221        0.0003  3.3329\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     23        0.0051       0.9170        0.1047        0.0003  2.8156\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     24        \u001b[36m0.0046\u001b[0m       0.9135        0.1152        0.0003  2.7672\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     25        0.0051       0.9139        0.1166        0.0003  2.7870\n"
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     "output_type": "stream",
     "text": [
      "     26        \u001b[36m0.0046\u001b[0m       0.9113        0.1169        0.0003  2.8207\n"
     ]
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     "output_type": "stream",
     "text": [
      "     27        \u001b[36m0.0045\u001b[0m       0.9202        0.1061        0.0003  2.8055\n"
     ]
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     "data": {
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "     28        0.0046       0.9117        0.1129        0.0003  2.7557\n"
     ]
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     "data": {
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "     29        0.0047       0.9112        0.1214        0.0003  2.8819\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "     30        0.0045       0.9127        0.1169        0.0003  2.7756\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "     31        \u001b[36m0.0040\u001b[0m       0.9109        0.1179        0.0001  2.7650\n"
     ]
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     32        \u001b[36m0.0035\u001b[0m       0.9153        0.1176        0.0001  3.2860\n"
     ]
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     33        0.0037       0.9120        0.1203        0.0001  2.8951\n"
     ]
    },
    {
     "data": {
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       "model_id": "",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     34        \u001b[36m0.0034\u001b[0m       0.9171        0.1044        0.0001  2.7940\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     35        \u001b[36m0.0034\u001b[0m       0.9132        0.1158        0.0001  2.8548\n"
     ]
    },
    {
     "data": {
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       "model_id": "",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     36        0.0034       0.9132        0.1078        0.0001  2.7690\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     37        0.0034       0.9099        0.1256        0.0001  2.7643\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     38        \u001b[36m0.0033\u001b[0m       0.9132        0.1112        0.0001  2.7507\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     39        0.0035       0.9135        0.1129        0.0001  2.7656\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     40        \u001b[36m0.0032\u001b[0m       0.9110        0.1164        0.0001  2.7597\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     41        \u001b[36m0.0031\u001b[0m       0.9159        0.1041        0.0001  3.2146\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "     42        \u001b[36m0.0029\u001b[0m       0.9178        0.1077        0.0001  2.7623\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     43        \u001b[36m0.0029\u001b[0m       0.9126        0.1161        0.0001  2.7596\n"
     ]
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      "text/plain": [
       "<class 'skorch.classifier.NeuralNetClassifier'>[initialized](\n",
       "  module_=DenseNN(\n",
       "    (layers): Sequential(\n",
       "      (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "      (1): LeakyReLU(negative_slope=0.2)\n",
       "      (2): Dropout(p=0.5, inplace=False)\n",
       "      (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (4): LeakyReLU(negative_slope=0.2)\n",
       "      (5): Dropout(p=0.5, inplace=False)\n",
       "      (6): Linear(in_features=512, out_features=64, bias=True)\n",
       "      (7): LeakyReLU(negative_slope=0.2)\n",
       "      (8): Dropout(p=0.5, inplace=False)\n",
       "      (9): Linear(in_features=64, out_features=3, bias=True)\n",
       "    )\n",
       "  ),\n",
       ")"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "callbacks = [\n",
    "    Checkpoint(f_pickle='models/classifier.pkl', monitor='valid_acc_best', load_best=True),\n",
    "    skorch.callbacks.ProgressBar(),\n",
    "    LRScheduler(policy=StepLR, step_size=10, gamma=.5)\n",
    "]\n",
    "\n",
    "classifier = skorch.NeuralNetClassifier(\n",
    "    module=DenseNN,\n",
    "    criterion=FocalLoss,\n",
    "    optimizer=torch.optim.Adam,\n",
    "    lr=1E-3,\n",
    "    max_epochs=100,\n",
    "    batch_size=1024,\n",
    "    train_split=predefined_split(Dataset(z_test, y_test)),\n",
    "    verbose=1,\n",
    "    device='cuda',\n",
    "    iterator_train__shuffle=True,\n",
    "    optimizer__weight_decay=1E-4,\n",
    "    callbacks=callbacks\n",
    ")\n",
    "classifier.fit(z_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "06b33b79-ea09-4e2f-a60b-97b373a94aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   20.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   53.7s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1, verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, verbose=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, verbose=True)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = sklearn.ensemble.RandomForestClassifier(n_jobs=-1, verbose=True)\n",
    "classifier.fit(z_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "60d348b9-af99-4c82-b179-2e1b8e24b5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_jobs=-1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = sklearn.neighbors.KNeighborsClassifier(n_jobs=-1)\n",
    "classifier.fit(z_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "14fe9a05-6e6a-4269-ae64-ac6f463d2962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9964    0.9126    0.9527     43960\n",
      "           1     0.3202    0.9447    0.4783      1828\n",
      "           2     0.9114    0.9478    0.9293      3202\n",
      "\n",
      "    accuracy                         0.9161     48990\n",
      "   macro avg     0.7427    0.9351    0.7867     48990\n",
      "weighted avg     0.9656    0.9161    0.9334     48990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, classifier.predict(z_test)\n",
    "bal_acc = balanced_accuracy_score(y_true, y_pred)\n",
    "conf_mat = confusion_matrix(y_true, y_pred)\n",
    "cm_train_plot = ConfusionMatrixDisplay(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1), display_labels=[num_to_label[i] for i in range(len(allowed_labels))])\n",
    "cm_train_plot.plot(colorbar=False)\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_true, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0ec504c7-d850-4cdd-80fb-1f5b0409256b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5309/803835625.py:28: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([''] + labels, fontsize=15)\n",
      "/tmp/ipykernel_5309/803835625.py:29: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([''] + labels, fontsize=15)\n"
     ]
    },
    {
     "data": {
      "image/png": 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o6Ogoh7eGzt71S/qWM7MtDSq68OlzOEcv3tdYZnr/+hgb6tNj4pYftvXWN4jQj5/IYp9amZYsmS4Z0qXk+r1nyrRp/erxzi+YycsPJc5B/GHu3LrJ/DkzaebamqLFS+Lt5ZmgdqInDyRPbqUxP0WKlADcvnUDUD8bPn3yOLdu3iAs7CNp06WjQqUq5NVwfye9neLE6fGjh5SvWBmARw8UwzXp0yvygoKCGDFkADVq16Vs+YoJOp6/zc0bNwDI65JPY350+q1bNxP8GtFXO1WqVVcJFgllZ2cPwMOHD8iaTTHZ5cGXzzo679mzZ0yeOJ4u3XrglCPHT7+mtv0zgeedbxAA9rYpNOY7pFWk29mq/uJ0alSCws4ZaTdiLb4xBK34qlYyJ3XK50FfLxl2aawo4pyRz+ERdBm3kafP1YdkqpbMSf2KLoxZtC9OVyeHzt2jasmcVC+Vi9PXHtHbtRypU5hz4MxdACoVc6JG6dw0G7Cc0I+fE+WY/iSRkZH07NoBy+TJGT1u0k+1ZW2dCiDG2WXRM5eePfPWmL9pw1qVf08YM5Kadeoxf/FylWGh3M55SZPGloXzZlOiVBmsrVMxavhgdHR0qFCpCgBTJ47F39+PCZOn/9Qx/U2i3/d06dJrzI9Of+al+fOLix8NsyVEqdJlMDY2ZsLY0eTK7QxRUUwYOxoTExNKlS4DwKABfTG3sGDYiFGJ8pra9s8MtZ25/hiAKiVyqE2dTpvKkvKFFWcW5iaGyvR0qS0Z1aUGJ688ZP3ey4nWl9xZ0+FaszBNqhaguEsmwj6H02fqNtbvvaJW1tTYgNmDGvLA8zXTVx6JU/sj5+8lNOwTW2e25/XJyQz6rzLu95+zzO0c+nrJmNavHkcveLD96A1lHQP9ZOjq6iTaMf7Oliycx7WrVxg9bjIpUqb8qbaKlSgJwLYtm/j06ZNK3vVrV7h75xYAQYGBKnmOjpkZM2EK567c5Nkbf2498GTJ8jXYpk3H7h1udP5PdcKBkZERo8dPxtvLkxKF8pLdMR3HjhyizX8dyZnbmYcPPFg0fw69+g0k/ZezYlDcx/p+aPlfEhykOOE0MTHRmB99Qz4wKFBjfmwuX7rEwwcPSJEiBVWrVU9YJ7+TJk0aBg0ZxrVrV8meOQPZs2TE3f06Q4aPxMbGhhPHj+G2dQsTJk7B3NxcWS8kJHFOjLXhnwk8R87f59q9Z5ibGrFzTicK5LTH1NiAwrkzsGNuJ/T0FNNRI7/5I505sCGGBnr0mLg5UfsyedkhjPP3JHnRvuRvNInVuy6yYFgTtsz4D3091WmxY7rVwC6NFT0mbYnx/s/3PDxfU7jJFMYvOcAyt3P0mbKVcu1m8Tk8gl6u5XBIm4K+UxWz2/JkS8fJlb3xvzAD33PTWDuptcq9oL/N82feTBgzguIlS8VpNllsGjRuRtp06Xn+zJtmDetw985tAgMDOXbkEK2aNUJPTzGo8P3U90ZNm9OtZx+yO+XA1NSUdOnS06BxU46eukCKlCnZu3snly9dUKnTsEkz9h89ReduPWnbvhMr1m5i6kzF+P7Avj1Jb2dP9179AEUgzOOUiXTW5mRMZ8340cOJjIz86eMVqqKvduo3aISBgUGitTtg0BB27tlPpy7d6Ny1O3v2H6JvvwGEh4fTp1d3ipcoSdPmLQCYN2c2DulsSGlpikM6GxbM+/3v+fwzQ20ATfstw212R/LntOf06r7K9FfvAhi3eD+ju9bAL0AxE6xOuTzULJObCUsP8MDzzS/pT9incO4+fknvyVuJjIykS5PSdGlSitlrjwNQIKc9HRuWZN2eS5y8/DBebXu/8lWZMg6Q3iY5A9tVZP6Gk3h4vsbEyIDtszsS8vETroNWYm1lxtjuNVk0oilN+i9PtOP8nfTv3Z1Pnz4xffaCRGnPzMyMjVt30qRBbY4dOcSxI1/vmTlmykzXHr2ZPWNqjPeAvpfG1pZmLVoxb/YMjh4+SMFCRVTyCxUuSqHCRVXSdu1w48SxI2zcthNDQ0Pcr1+lQ5sWlKtQiYlTZ3L29EmmT5mIdarUdOzS/ecP+g9i+mW4Mqargeh7dOZm5hrzfyQ8PJxtWzYB0DSRhtm+ValyFSpVrqKSNm/ubB54eHD+kmJSy47tbvTv24umzVpQr0FDtm/bSt/ePbB3cKBGzVqJ3qfE8k8FHu9XvhRuNoXaZZ0p4pwRI0N97j15ycb9V6ldTvFMzN0nimdoqpVSrCRQvnA2SrhkUmnHOZtiAsL0/vUJCAplzZ5LrN196af6tn7vFbo0KU2N0rmUgady8RwkS6ZLzsxpObi4m0r5rBlsABjYriJt6hTh8Pn7TItlKG5ynzr4B4YyYelBAJpUzY9tKksqtp/DmWuKociUyU0Z1rEqmexS/ZWz3Q7u34tl8uT07dlFJf3jx48AvHzhQ80q5QD438r12KRJE2ubuZzzcMn9Lju2beHmjetERETgnNeFeg0aM2ua4h5S9njcAHbMrJhS//rVq1jLhoaGMnxwf6pUq0GlKoqhnvmzZ2JqZsbyNRsxNzenWo1a3LxxnTkzp/1zgSf6ZryPz3ON+dHpdnGY5vy9I4cP8ebNGzI6OlK0WLGEdzKOXr16xYSxo+nQqQu5nRXfV7NmTCOjoyP/W7EKXV1dqlWvwblzZ5gxbYoEnt9JREQkbkfccTvirpJexDkjAKevPlJJL/wlXZO82RU3Jk99Vych3vkpxqKtrczU8qJfR5PsGdOQPWMavF5qXk4jWpmCWalXwYXWQ1cTFBIGKJYPArh69+uN7yt3vL+0a/NXBh4Afz8/zp4+pTHv48ePyrywsI9xbtPExIRmrq3Uhu8uXTgPQPGS6tOtY+yfr6+yzdjMmDqRt29eM2HKYWXawwceZM2aTWX8P1/+gpw9fYqAgIAYV1H4GznnyQOA+5dp79+LTs+dW/PD2D8SPczWtFmLBPYufoYM7I+xsTEjRo1Rpj3wuE/Z8hWUQ7m6urrky1+A40fjdj84qfxzgUcTm5Tm1C2fl3d+Qew4pphW2WHUejqMWq+x/MHF3ShVIAvl281OtOd6otdSe/LNrLbxSw6oPewabcmoZrjWLEybYWvYuF99UsK39PR0mTGgPqevPmLTAfVngEyMDJSz20y/TDuP/EtvSH8I1rwskLeXJ3lzZCajYyau3or5qfL4uHPrJmfPnCK7U06KFC0epzpRUVHs2b0DgDx5NU8Bjvb0yWPmzZpOjz79yZBR9Wn5kJDQ7/6tGGqKfvj0X1G0WHEsLS158vgxN9zdyZM3r0r+9m1bAahWvWa82g0KCmLPrp2AdgLP2TNn2LB+LYuXLld5lgsg9LthxJDg4AQt6aVN8e6djo4OOjo6JE+eXOXBtW9NmjQJHR0dRo0a9ZPdS1w5MtmqLYSZLrUlW2b8h4WZEYNm7uBj2M9NL06byhL3bUNw3zZEJd06uSlt6hbF2Ej9ob5yhbMxvofisnjNros/9fqadGtamiz2qeg9ZatK+t0niqGcbxcabVRZ8WV370nswzz/kqWL5lPYJSdjRgxRy7t1w53wcNWA5nH/Hq2aNyIqKorJ02ep5L17+5b/LV5A4Hcz3YKCgujbsytXL1/CxiYNNWrX/WGfBvfvjU0aW3r1HaiSnt0pBx7373LT/ToAgYGBHNi3h/R29ipXQf8CAwMDOnVRDFP36tFVZdHW2TNncOvWTUqWKk2+/F+fnVo4fx55cmX/4aKbO7e7ERISQqHCRcicJebVRhJDREQEfXp2o1DhIri2aq2S55QjJ6dOnlAudurj48PpUydxyhH3RYeTQoKvePz9/ZkxYwZjxoyJvfBvopdrWWqVdcb9/nNevQsglZUZxfI6YmSoz4SlB1i35+enTOvpJVMOYX3LxNiQBcOaMLVvXa7fe4bPG39MjAzI4pCK7BkV9xHmrD3OjmM31Or+jDTWFgz+rwqLNp9WWwNu04GrDO1Qhcm961ChSHask5tSIJcDmw9cxdPn55Z4+du8f/+Ohw88NN53GTKwDx737pEztzPW1qnw8XnG5YsX0NHRYcbchZQsXValfEhIMAP69GDMiCG45CuATRpb3r17y80b1/nw/j2WyZOzYt2mHw61Hdy/h0MH9rFm4zaMjVVnIXbv1ZetmzdQq1oFSpUuy80b7vg8f8aMOYkzoeJPM2jIMI4dPcKF8+fI5ZSF4iVK4u3lxeVLF0mVKhWLl6pOpHn//h0PPDx49TLmNRPj8+zO9WvX6Nn96z1F7y/PDDVuUBcDQ8XjG23a/kebdv9prL9k0UJu377F6XOX1K5Y+w0YRL3aNSheOD9FihXnwrmzBAcH03/g771SdYKux3R0dDAyMmL27Nn4+vrGXuE3sfvELS7e9CRrhtTULZ+HnJnTcvj8PSp1mMvYRftjb+AnvPUNZMisnZy6+gi7NFbULJObikWzY2Sgz+YDV6nUYS4DZ+5I9Ned0LM2IR8/MXax+vF9DPtMza4LOX75AaUKZCaTfSpW7DhP1/GbEr0ff7OGTZqTzcmJO7dusmvHNryePqVu/UYcPXWBVm3Uv0ysUqSkZ5/+5HHJx6NHD9m9041LF86ROnUauvbozdnLN344NBcWFsaQAX0pV6ES1WvWVsvPmduZtZu2YWfnwMH9e4mIiGDkmAm0btchUY/7T2FkZMTBI8cZPHQ4JiYm7N65g2feXri2bM25S9fitDL1t16+fMmJ48fQ19enQaPGsZYPDAzg8qWLyp/oiSw3brgr02Ka/PDu3TvGjh5B23btVa7KolWtVp1FS5ZhbmHBvj27MTM3Z8n/VqjNhvvd6ETF8+kyHR0dkiVLRteuXZkzZw5Dhgxh/PjxKmUmTZrE4MGDGTlyZJyH2wICArC0tMQwT0d0khnGXkH80V6cnpHUXRBaZGyQLPZC4o8XEBCATUpL/P39fziJJcF3oAYNGoSxsTFz587l/U+uvCuEEOLfkeDAY2trS6dOnQgMDGTq1KmJ2SchhBB/sZ+aczdw4EBMTEyYN28eb9/+nc98CCGESFw/FXhsbGzo3LkzwcHBTJ48OV51w8LCCAgIUPkRQgjx9/vpp4wGDhyIqakpCxcu5PXr13GuN3HiRCwtLZU/dl/2GxFCCPF3++nAkypVKrp27UpISAiTJsV9b5PBgwfj7++v/Hn27FnslYQQQvzxEmVdhf79+2NmZsaiRYt4+YOHrr5laGiIhYWFys/fwskxDesmt8b7yHg+nJ3K5U0D6da0dLyXKzm4uBuhV2f/8Cf48sxY2ynukongyzMJvTqbBcObJPSwhAb37t6hdYvGZHFIQ9qUZhQvmJeF82bHewuCmlXKkcJU74c/Kc00b2Xt4/Ocrh3bkSOTHbYpTCmYx4mJ40YpnxcRiefunTs0a9IQO9tUWJkbUyBvbubOnhXvz9vj/n2mTZ1M5QplSZ/GGnNjfTKkT0PjhvU4c+b0D+tu3bKZKhXLYZvKCnNjfRzS2dCwXm1OnTzxE0emXYmyVpu1tTXdu3dn4sSJTJw4kbRp0yZGs3+kwrkzsG9RV0yMDLh82xOvFx8oni8TU/vVo0iejLQYtDLObR0+fz/GxT9dnOzIlTktZ6//eK04A/1kzB8W+0NuIv4uXTxP3eqVCA0NJV+Bgtg7ZOD8mdMMHdiXSxfPs3z1hjifbJSvUBl7+wwa89yvX+Pe3dsULV5CLe/J40dULleC9+/e4ZQjF0WKl8D92lWmThzHqRPH2LH3MIaG8lxcYrhw/jzVKpcnNDSUAgUL4ZAhA2dPn2JAv95cuHCOtes3xfnzrlalAi98fDAzM6NQ4SJYpUjB/bt32bVjO7t37mDy1Bl079lLrV7/vr2ZN2cWenp6FC9REutUqXjy6BF7du9iz+5dzFuwmHbtf/8HhRNtkdC+ffsyb948lixZQvv27ROr2T+Knp4uK8a5YmJkwIDp25m7/gSgWHhzz/wu1K/owoGzd+O8hcKPtjk4tao3AOv3/XiZn0H/VSaLfSpW7rxA27q/fun2f8Xnz5/p2LYloaGhjJs0jS7dewGK9dbq16rCTretbKhUNc6bzfXqNzDGvAqlFfvvNGqqvhhl145tef/uHR06d2fSNMXVb3h4OG1dm7Bn1w5mTpvEoKEj43l04nufP3+mTavmhIaGMnnqDHr0Uvz9BQUFUaNqJdy2bmFtlWpqa6nFJFu27IwdN5F6DRpiZGSkTP/fksV079qJwQP7UaFiJZy+2U7j1s2bzJszi+TJk3Ps5FmVvM2bNtLatRkD+/ehcdNmKtum/44SbQnTlClT0qNHD8LCwli2bFliNZvk7G1TEHp1NktGNYu1bO2yzmRMb80Nj+fKoAMQHPpJuUBnzxZlY6gdd5nsUlEwVwZCP35S297hW06OaejTsjwrd17g/I2nP/26fztvL09SmOrRtUPbWMvu2bUDL8+n5MqdRxl0QLEx3JTpcwCYPyf2YdDYPH70kGtXLmNkZESdug1U8q5eucTF8+dIlSo1o8d/vb+qp6fHtFnz0dfXZ8nCeWoLmAoFL09PjPV1aN+2daxld+7YjufTpzg751EGHVB83jNnzwNg9szpcX7tfQeP0KyFq0rQAfivQ0cqVKxEREQEbtu2qOSd+bJdR4OGjVWCDkCjxk3IlSs3wcHB3Lt7N879SCqJunZ23759sbCwIDQ0NPbCf6EqJRQrwm4/qr7Qp/v95zx5/o5cmdNib5vip16naTXFatL7Tt8hICjmcfz5QxvjHxTKsDm7f+r1hLrDB/YBUKtuPbW8PC75yJDRkXt3b+Pt5flTr7N54zoAKlergYWlpUreoS99qFythtpwWmobG4oWK4Gfry8Xzp35qT4IOLBvLwB16zdQy3PJl4+Mjo7cuXMbL0/Pn36t3M6KPYRevnihkh7XIdOUKVP+dB9+tUQNPFZWVvTq1Ssxm/yj5M6iuLflfl/zgn/u95+plEuoJlUViwVu2BfzPjwdGpagaF5HBs3cgW+A5m1/RcLdvqXYtylPHheN+c55Fel3bt/6qdfZumkDAI2aNFfLuxPdh7y/tg8Cbt1UnEzmddG8R1J0+q0vn8nPePpUcd/2+91vS5cpi56eHlu3bFK7qtm8aSO3b9+iZKnSOGZS3TH5dxTvezyxrSk6evRoRo8eneAO/cns0lgB4PPaT2O+z2t/AOxtrRL8GoVzZyCTXSre+QVx8JzmS+q0qSwZ060GJy4/+GFwEgn3/Llip9a06TTvDps2rWJ79GfeXgl+jUsXz/P0yWNSpExJhUrqqw0///IIQox9+JL+M30QCs+eKT7vdDG819Hp0VseJNSTx4/Zv3cPANVrqG5dnSlzZqZMm0m/Pj0plD8PxUuUJFXq1Dx++BB39+tUr1GTRd9t8fC7kh1IvxG9s6gmrjUL41qzsFp6+1HrlJMFzEwUl8IhHz9pbCM4NOxLOSON+XHRtHpBALYevEZ4uOYpnDMHNsDIQJ8eE7dozBeK6csxbX+9Yd1qNqxbrZY+b9Ey5WSB4CDFVuXGMeyZY2pqCkBQUKDG/LjYvEExzFavQWP09dWnUgcHf+nDd/vxRDNR9iEowX34W1QqX4bTp05qzFu7ZhVr16xSS1/yvxXKyQLR72FMeyQlxucdHh5O+3atCQsLo0Gjxhq3QejctRvWqVLRqX1bTp44rkxPY2tLufIVSZHi54bxtUUCzzc0TV82MzakboW8PH72VuM210+evdNW99DT06V+xbwArI/hSqZ2WWdqlXVm/JIDPPR6o7W+/Wk0TV8OCg5i9w43Mjpm0rgfjjaHMD5//swON8WJQ6Om6sNsIn4qVq6Cg0MGlbSg4CB2uG3DMVMmihVTn6rumCmzlnqn0LdXD86dPUNGR0dmz1XftC8qKor+fXszf+5s2nfoRI9efbBNm5a7d+4weGA/+vTqjsf9e8yaO1+r/U4ICTzf0DR92d42BXUr5OWc+xM6jFr/w/pBIWGksNTDxMhAY76pseGXcgl7sK9ysRxYJzfjodcbLt9Wv6Q3NzVkxoD6PPR6w5TlhxL0Gv8KTdOXvb082b3DjSJFizN/yY+HLEzNzPDz9VXb7z5a9BbLZmYJ22r6yKEDfHj/nkyZs1CgoPqVNoCpqWLKbEyTeUKUffi9p9ZqQ/8Bg9TSvDw92eG2jWLFSrB0+cof1jczM8PX15eQX/R5T544niWLF2JjY8PuvQc1XrmsXb2K+XNnU6NWbebMX6hML1ioENt37SVPruwsWbyQDp26kCPn7731daJOLvjXPXul2I01nU1yjfnpbBSzkrxfJmzX1ujZbBv2a77ayZvdjrSpk6Ovl4zd8zpzcHE35U+/1hUAqFIiBwcXd2P1hLg9XyI0S5/eHoAXMewc+eKFDwB29g4Jaj96NlvDJjFP40//ZX3DGPvwJT2hfRBf2dkpPu+YdgqNTrd3iP97vXTxIkaNGIalpSU79xwgU2bNV1rr160BoF499Zl15ubmVKpUhaioKM6d/f1nMUrgSUS3HiqmP+bNrvkGZN7sdirl4sPc1JBqJRVnMbFNGMiQLiWlCmRR+cmWwQYAW2tLShXIQqHc8mX0M3Lldgbgxo3rGvNvuivSc+bKHe+2AwICOLhPcYNZ02y2aDmj++Ce+H0QqqKnOLtfv6YxPzo995fPJK42b9pIrx5dMTExwW3nXvLkzRtj2ejg9v20+mjR6b5+CTux1SYJPInowJk7ANQtn0ctL0+2dDimt+b2oxd4x7AMzo/ULZ8XYyMDzrk/wdNH846vp68+wjh/T40/7UcpzqBX7DiPcf6eZK85Jt59EF9VrFINgF3b3dTybrpfx/PpE5xy5ML+u/sKcbFr+zY+fvxI4aLFyJDRMcZylb704eC+PYSFhankvXn9mvPnzpDcyorCGu5XifipUq06ANu3bVXLc79+nadPnpAzZy4cMmSIc5sH9u/jvzYt0dPTY9PW7RQr/uPPycZGMb362lXNJ57R6d/fy/odSeCJhffLDxjn7xnr/R2Ancdv8vT5O/JkS0/3ZmWU6SZGBswa2BCA2WuPq9Xbt7Ar7tuGUCCnfYxtRw+zrd/74yVyRMLZO2TgQ3B4rPd3AGrUqoNDhozcvnWDBXNnKdODg4Pp36c7AF179FarV6daRQq75OTqlZiXTdryZZitsYYlcr6Vv0AhChctxtu3bxg1bLAyPTw8nH69u/H582c6dO6mcUacAIcMGQj9HBXr/R2A2nXqkiFjRm7evMGcWV9XpAgODqZXj64A9OzdV61e1UrlyZMrO5cvqX7e586epVnjBkRFRbFm/SYqVKwUax9q1qoDwJxZM9TaWzh/HmfPnMbc3DxObSU1mVzwjX6tK5A1Q+p41Vm544Jytlt4eCRth69h78KuTOlblwaVXPB++YHiLpmwTWWJ25HrGtdpc0yfEoe0KTGOYVJCutSWlMyXmbBP4Ww7rHlYRcTPrGmTefjAI151XFu3pciX2U/6+vosWraKejUqM2xQP7Zv24ydvQMXzp7h1auX1Kpbn6YtWqq18fTpE555e8U4KcHH5zlnz5zCwMCAOvUaxtqneYuWUblcCRYvmMPpk8fJ5uTE9atX8Hz6hEJFitK7n/pN9X/R1CmTeHD/frzqtG77H8VLfP28l69cS/UqFRjYvw9bt2zC3sGBs2dO8+rlS+rWb0CLlur3TZ88eYy3lxehoaqfd/06NQgNDSVDxozs3rmD3Tt3qNUtVrwEbdr9p/x3h06d2bnDjXNnz1CmZFEKFymKbdq03Lt7h3t375IsWTJmzZn/R0yplsDzjYpFs8f4HE9MTl19pDLN+sJNT0q4Tmd4x6qULJCZ3FnS8uT5e2auPsa8DZqfI4hN4yr5SZZMlz0nb+EX+G8uR5TYjh45GONzPDEpXrK0MvAAFC5SjCOnLjBp/GjOnj7JnVs3yZAxE9169aVT1x7x3gYDYNumDURGRlKpSjWSW8X+oHGmzFk4ce4KE8eO4tjhg+zd5UF6O3v6DRpKn/6DZWXqLw4fPBDjczwxKVW6jDLwABQtVowz5y8zdsxITp88wa2bN3DMlIneffrTrUfPeH3efn5+AHg+fYrn05jXUfw28BgZGbH/0FEWzp/H1i2buH3rJpcvXcQ6VSrq1m9Az159KVykSLyOManoRMW2FIGWBAQEYGlpiWGejugkkz+Wv92L0zOSugtCi4wNkiV1F4QWBAQEYJPSEn9//x/usSb3eIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBapZfUHfiex4GJWFhYJHU3xC+WtvzQpO6C0KK3JyYkdReEFoRHRMapnFzxCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwfFGzSjlSmOrF+HPk0IF4tefn68ugfr1xzu5IGisTnLM7Mrh/H/z9/NTKent5/vC1o3+6dfpPpd6H9+/p0MYV+zRWONimoFO7Vvh++KCxP16eT0mb0owxI4bE6zj+dNkcUrFiVGOe7B6C38lx3HcbyMy+tUhpaaJW1iVbOoa2q8DxJZ15eWgk/qfG8XDHIJaPbEyuTGni/drVSzixdHhDLq/txbP9wwk4PR6vvcPYPr01VYtn11inpIsjoecnxfhzcmkXjfU6NSjKvW0D8Ds5jjPLu1HSxTHGfu2c2Ya7W/tjaKAX72P604SEhLB71w66dPwPl9xOWFuaYJPCnKIFXZg0fixBQUHxai9nVkfMjZLF+OPhcV9jvYcPH9CpfVtyZMlICnMjbK0tKV28MPPmzOLTp09q5b08PWlQtyaprczIkN6Gfr178PHjR41tX7xwHgtjPZYvXRyvY0lKf/9vXjzVrFMPM1MztfS0adPFuY33795RuVwJnjx+RIaMjlSrWZv7d++yeMEcjh4+wMFjZ7BKkUJZ3tTUjKbNW8bY3vZtm/n48SNFi5VQSW/fpgXHjx6mWImSREVFsXnjOt69e8vWnfvU2hgyoA8pUlrTd+DQOB/Hn650/kxsm9oKU2MD7nu+4eItb3I42tCpQTFqlMxBmfYL8HkbAECyZLqcW9kdgPf+wVy45UXIx0/kyZqWplVcqFcuN21GbWT78dtxfv3mVfNRu0xO7j59w+U7zwgKCcPB1ooqxbJTpVh2pqw6zshFBzXWffz8HedueKmlP/F5r5bWqGIeZvatjfdLXw6d96B0gUzsmtmGPE1m4P3KV6VsrdI5qVQkGw0HrCbsU3icj+VPtXnjerp36QhAtuxOVKtRk8CAAC5eOM/4saPYsnkjBw4fJ1Xq1PFqt1kLzX+vlhaWamkXzp+jdvXKhISEkC27EzVq1SbA359zZ88weEBf9u7exe79h9DTU3wdR0REUL9ODR543KdCxUq8efOGxQvn8/nzZ2bPW6jSdmRkJH17dSevSz5at2sfr2NIShJ4vjN2whTsHTL8VBtDBvThyeNH1Khdl+WrNyh/oQb168WShfMYNqgf85csV5ZPaW2t8u9vedy/x4Z1qzE2NqZmnXrK9GtXL3P86GHa/NeR6bPnA9Cza0fWrFzG9WtXcMlXQFn2yKED7N+7m2WrN2BqavpTx/anMDbUZ+XoJpgaGzB+2RHG/e+IMm9Ct6r0bl6ahUMaUKv31/f9yt1nTF55nH1n7xEZGQWAjo4OI9pXZFCbciwa2oBT157w3j8kTn2YvOo43SZv50OAavmCOezYO+c/+rmWZvNhd+48fq1W99wNLzqM2xKn1xnYuhwv3wVQqOVs/IM+UszZgaOLO9O7eUl6T9+lLGdkqMfkHtU5cO4+e07fjVPbfzp9fX3atGtPl+49yZ7dSZn+6uVLGtStyQ336wzs15vlq9fFq93F/1sR57L9evcgJCSEUWMn0Lf/QGX6yxcvqFy+NGdOn2TDujW4tmoDwK4dbnjcv8focRPp028AkZGR1KtVjdUrlzN46AjS2Noq21j+v8XcvOHOkRNn0NX9cwaw/pye/iFevXzJti0bMTAwYNrMecqgAzB6/GSsrVOxeeM63r55E6f2tmxU/EFUrV4LCwsLZfqtmzcAaNLcVZnWvGVrlTyAT58+Mbh/b0qVLkvd+g0TfFx/mtplcpImpTkeXm8Yv+yoSt6IhQfxfPGBikWykjuz4o84IiKSku3ms+f0XWXQAYiKimL0kkN4eL3BwtSIKjEMkWly48ELtaADcPnuM7YevYmuri6l82VK4BEqGOgnI5tDKvacvot/kGIo5txNLx54vcU5S1qVsv1cy2BrbUG/mbt/6jX/JM1dWzFn/iKVoAOQxtaW6bPmArBr53aNw12JISgoiBvu1zExMaF33/4qebZp09Khc1cArl29oky/eUPx99v8y1WVrq4uzV1bER4ezr27d5Tl3r9/z5hRI2ju2opChYv8kv7/KhJ4EtnRwweJjIykaLESpLaxUckzNDSkcrUaREREcPjg/ljbioqKYuvmjQA0btpcJc/fVzGEkjy5lTIt+v/9fL8Or8yfPQMvz6dMmj47YQf0h8qXPT0AZ64/JSoqSiUvPCKS8zcVw1g1SuWIU3u3H70CwNbaIpaScfM5PAKAT1/+m1CWZkYkS6aLX2CoSrpfYCjJzY2V/3awtaJPi9LMWn+Kx8/Vh+v+Rbmd8wAQFhbGh/e/5j3R19eP05VIihQplf/v5/flb9vqm7/tL/8fnQcwesRQoiIjGTNuYmJ1V2tkqO07a1Ytx/fDB3R1dcmUOQvVa9YmvZ19nOvfvnUTAOe8Lhrz8+R1Yd3qFdy5fTPWti6cO4O3lyepUqWmbIVKKnnpvvTp8aMHZMmaDYBHDx8AKPv7/PkzZkydSIfO3cjuFLcv2L+FiZE+gNoXcrQPX4bLnDPbasz/Xoa0intyr98H/nTfcmayoUEFZz59DufopYcay2S2S8mYzpVJYWHCe/8Qzt3w5NCFB2pB9K1vMKFhn8lil0qZliyZLhnSpuC6h48ybVrvmrzzDWbyyuM/3f+/hefTJ4AiOHx7zzUuZs2YxtMnjzE0NCS7Uw5q1q5LqlSp1MoZGhpSvEQpTp86wczpU9WG2pYuWoC+vj5NmrdQpkf//T56+ICcuXID8PCB6t/2tatXWLViGZOnzYz3/anfgQSe70yfPEHl3yOGDKDfoKH0HzQsTvV9nnsDkDZdeo350enPnnnH2tbmjesBqNugscqQHUCJkqUxNjZmyoRx5MzlTFRUFFMmjMXExIQSJUsDMHxwf8zNLRg4dGSc+v43eecXDIB9GiuN+Q5pFel2aZLH2lYxZwfyO6Un7FM4hy88iHdfqpVwok6ZXOjr6WJnk5wiuR34HB5Bl0luPPXRPAuxqHMGijpnUEm79eglTQevVbtiOXTeg6rFs1O9hBOnrz+hd/PSpE5hxoFzihlWlYpkpUbJHDQbspbQsM/x7v/fasG8OQBUqFQZQ0PDeNUdPmSgyr8HD+jL1Bmzadm6rVrZWfMWULtaZUYNH8KGdWvIkTMngQEBnD1zmjRpbNm6fTdZsmRVlq9UuQqjRwxlxLDBLFqynDdvXjN/zizSpktHbuc8REVF0bd3D3LkzEX7jp0TcORJTwLPF0WLl8S1VTsKFSmKTRpbfJ4/Y9eObUyfPIGJY0dhbm5Bp649Ym0nenqmsYn6dF0Aky/pQYE/PnMOCwtj5/atADRu1lwt3yZNGvoOHMK4UcPJ4/T1PsGosRNJbWPDqRPH2Om2lUX/W4W5ubkyPyQkRNmHv9kZ96cMBKoUy05KSxOVCQFpU1lQvlAWAMxNfvyFY25iyKKhDQCYu/EMrxJwxZM7sy2u1fMr/x3y8RP9Zu5m/f7ramUDgj8yY+1Jdhy/zaPn7wBwzpKW0R0rUTi3A3tmt6Nwy9kEBIcp64xcdJDS+TOxdWorZZr7gxcs23EJfb1kTOtdk6OXHqrMyDPQT0Z4RKTK/ax/ycED+1i9cjn6+voMHzkmzvWq1ahJydJlcHHJj3WqVHg+fcLqVStYOG8O3Tp3IEXKlNSoWVulTtas2Th07BTNGtfH/fo1PO7fAxQTV0qWLoNTjpwq5XM756FVm3asXP4/HO1tlWVXrF6HkZERq1cu58qlixw4coJkyZIp64WGhmJsbMyfQCfq+2v3JBIQEIClpSWeLz+o3ERPaseOHKJB7WpYJk/O3UfPYv1g69WswoljR5g1fzEtW7dTyz9x7Aj1alahTLkKuO2O+dmg3Tu306pZQ7Jky87FazFP4T1y6ACHD+5HR0eHylWrU7Z8RcLDwylZJB8pUqRg76ETACyaP4eZUyfx9u0bUqVKTd+BQ+jQuVvc3oRfIG35Xz+t++yKbuTLnp6rd5/Ra/pO7j19Q65MaZg7sC7ZM6RGXy8Z9z3f4NJ0hsb6uro6bJ7ckuolnLh8x5vynRYr780khKGBHpnSp6R9vSJ0ql+UvWfu0XTw2ji1qaurw8F5HSjhkpHhCw8wbfUJlXz7NMlpWaMAaVKac+fxa1buvkxo2Gf6tyrDsHYVKOQ6Gw+vt+TJmpY5/etQKJc9nz6Hs/vUXbpN3h7jkGRieXtiQuyFtMTD4z4Vy5TA19eXydNm0qVb7CeUsVmxbCk9unYiS9ZsXLupOmPw5PFjtGjakHTp7ZgybSYu+Qvw4f17Vq9czrQpE7Gzs+f4mQsqQ3VRUVG4bd3MmdOnMDI2pn6DRhQoWAg/Pz/yOTtRvnxFlq5YTWRkJGNHjWDp4gX4+/tjb+/A2AmTqNeg0U8fU0IEBASQLrUV/v7+P/welyueWJSrUAmXfAW4fu0KVy9fpESpMj8sb2ameAYoNETzlNuQL+lm31yFaBI9m+37SQXfq1CpChUqVVFJWzR/Do8eeHDi7GVAEcSGDOhDoybNqV2vAbu2b2NQv17Y2TtQtXrNH7b/J2s6eC1u01qTP4cdp5d9DbKv3gcy7n9HGN2p8g+/cOf0r0P1Ek54eL2hbt+VPxV0AMI+hXP3yWt6T9tJZEQkXRoVp0vDYszecDrWupGRUUxfe4ISLhmpWDirWuDxfuWnMmUcIH1qSwa2Ksf8zWfx8HqLiZE+26e1JiTsE67D1mNtZcrYzlVYNKQ+TQav/alj+1O88PGhXs1q+Pr60q1n70QJOgCt2rRj7OgRPHzggZenJw4ZMgDw4cMHXJs35vPnz7jt3EvadIrnAc3NzRk+agwBAf4sWjCPObOmM3b8JGV7Ojo61G/YmPoNG6u8zrjRI/gYGsq4iVMAmD93NtOmTKRr956ULF2G5UsX06ZlczJnyYpznryJcmy/gsxqiwPHTJkBeP3qVaxl06VX3Px74fNcY350ut0PJiz4+/kpr2IaNm4Wr76+fvWKKRPH0q5DZ3LmdgZg3uzpZMjoyIKlK6havSbzlyzHIUNG5sycGq+2/zTer/wo3GoOzYeuY+7GMyzdfoE+03eRr9kM3nxQDInefaL+DA3A2M5VaFenMM9e+VGjx7I4P7sTV+sPKIbZapSM+6SPR88U93bSpPzxSUu0yT2r4x8UyoTliunkTSq7YJvKgk4TtrH16E0WbT3PrPWnqV0mF5nSp4yltT/fhw8fqF2jCt7eXrRo2ZoJkxLv919XV5eMjooh71evXirTD+7fi++HDxQsVEQZdL5Vp55iGPfsmdhPPm7fusn/lixi8LCR2KRRrKQxZ9Z0SpYqw6SpM6heoxYr127E1NSUOTOnJ8Zh/TJyxRMH0VMYTeLw8GWuL1/2N93Vx+8BbnxJz5nLOcY2trttISwsjGIlSmJn7xCvvo4cOhBjI2MGDxulTHv4wIPSZcsrp3Xq6uqSN19+Th4/GkMrf4+IiEjcjt3C7dgtlfQizor39fS1J2p1+jQvRb+WZXj9IZDqPf/H8zf+id6v6MkP1lZxf6DX6sv06OCPsT9zUqZAJuqVc6b1yA0EhSjKZ3NQDOVcvfv1pOjK3WcAZM+Q+q+eZh0UFET92tW5f+8uterUZd7CJejo6CTqa0Q/xvDtQ9o+PoqZhRaWmoedLC0tVer+SN9ePciSNRuduypW2AgICODVy5cqoyLm5uZkzZad+1/uI/2uJPDE4t3bt1w4dwaIeYr0t8pXrIyuri7nz53h7Zs3KlMdw8LCOLhvD8mSJaNi5aoxthE9zNaoaYsYy2hy4dwZNm9cx9yF/8MyeXKVvO+H/kKCg/+oJ50Tk00KM+qWzcU7v2B2nFS9f9amVkHGd6uGb0AotXot56H3u1/Sh+i11DQtgROTOmVzAeDu8eKH5fSS6TKjTy1OX3/CpkM31PJNjPSVs9tMjQ0AiPw9bvX+EmFhYTRpUJcrly9RoWIlVqxer3JTPjHcu3uHhw88MDExIWu2rw8Z23x5lu+muzsRERFqr3v1imI43MHhxyeYmzas49zZ0+zZf1hthuv3f9vBwcG//SSDf/Ob5zsXL5xj7+6dRESojuF7e3ni2rQ+wcHBVK1ek3TfTJFeumg+hV1yqi26mcbWlvoNm/Dp0yf69e5GePjX9bBGDh3Eu3dvadSkeYxz7595e3Hh3FmMjIyoU7dBnI8hIiKCAX16UqBQYZq5tlLJy+6Ug7OnT/LiheLs68ULH86dOfXXP9uTw9FGbSHMdKks2DKlJRamRgyau5ePYV8/n7plczF3QF0Cg8Oo23cFNx++/L5JNWlTWeC+sQ/uG/uopFsnN6VNrYIYG+qr1SlXMDPjuypOPNbsuaqS161xcdKnVl/vq12dQnRvUoLIyEiWul34YZ+6NS5OFjtrek/fqZJ+96liWLFJ5a8nUI0qKh6ivPc0bitp/GkiIiJo07IZJ08co1jxkqzbtA0DA4Mf1lm8cD75nHMwcpjq3/bBA/s4efyYWvnbt27i2qwxUVFRtGrTTqX9ipWqYGhoiKfnU8aOGkFkZKQy78EDD8aPHQVA7br1Y+xPYGAgw4YMpF6DhpQuW06ZbmFhQbp06dm3ZzeBX2bJ3nC/jsf9e7/93/ZPX/EcP36cefPmceHCBd6+fYupqSmpU6fG2dmZ0qVL4+rqqryc/F09fviQbp3aYWOTBue8LlhaJufZMy9uXL/Gx48fye6Uk1nzVFd+ff/+HQ8feGi87zNhygyuXL7I7h1uFHbJSd58+bl/9y737t4mU+YsjJs0Lca+bNm0nqioKCpXq4FFPN635UsXcffOLY6cOq82hNCr7wCa1K9NueKFKFS0GJfOnyM4OJhefQfG0NrfoVezUtQqnRN3Dx9evQ8klZUpxZwzYGSoz4TlR1m375qybCorU1aMakKyZLp4vvxAuzqFaVensFqbu0/dYfepr7OW9JIlI5uD+kmEiZEBCwbXZ2qvmly/74PPW39MjAzIYm9N9gyK8nM2nGbHCdUrrm6NSzCxWzXcH7zA88UHDA30yZXJhozpUhIREUnfmbtVHgz9XpqU5gxuW55F286rrQG36ZA7Q9tWYHKP6lQonAXr5KYUyGHH5kPueL7Q/DzRn27xwvns3rkDgJTWKendo6vGcuMnTcXa2hpQLPL78IGHyr0agKuXLzNx/Bjs7R3I5eyMibEJnp5Pcb9+TTGTtFQZRn+3ikAaW1vGT5pK/z49mT51Em5bN+OcNy8f3n/g0sXzhIWFUalKVVp8We5Kk4njxxAYEMAEDd8bffoPpG+v7hQtkBfnvHk5deI4urq6asvz/G5+KvCMGTOGkSMVDyc6OTlRuHBh9PX18fDwwM3Nja1bt1KgQAGKFPm91xHKX7AQbdt34urlS1y/egU/P19MTE3J5ZyH2nUb0LZ9p3hduqa0tubIyfNMnjCGvbt3snfXDlKltqFD5+4MHjZSbRjsW1s2bQCgUZMfz2b71vt375g4bhQt2/xHXpf8avmVqlRnzoKlzJ4xhYP79mBn78CIMRPUZsP9bXafuoNNSjNyZ7alqLMDvoGhHL7wgLmbznL6uuq9HWNDA+XVUe7Mtso13L7n9dJXJfDE5K1vEEPm7aOkiyM5HG3I55QOXR0dXr0PZPMhd/6345JaHwBmrz9N+cJZyJExtXLK96v3gazff40FW85x9Z7mSSvRJnSrRsjHz4xdelgt72NYODV7LWNa75qUyudI2KcIVuy6zIDZf+/abd/eO4kOQJoMGTZSGXhiUr5iJZ4/f8a1q1e4cP4cAf7+mFtYULRYCRo1aYprqzYah/A6du5Kjhw5WTB/DpcvXWTv7l2YmJjgnNeFps1a0Pa/DjEO/d2/f49F8+cydMRo0qVXfyi9fcfOBAYG8r/FCzmwby9Zs2Vn1Njxas8G/W4S/BzP1atXKViwIHp6emzevJk6deqo5L969Yq1a9dSo0YNsmePfWHF3/U5HvFraOM5HvH7+J2e4xG/zi9/jsfNzY2oqCgaNWqkFnQA0qRJQ79+/RLavBBCiL9UgicXvH37FkDjwnhCCCFETBIceOzs7ADYtm0bb+K4t4wQQgiR4MDTvHlzjI2NefbsGZkzZ6Z169b873//4/r162rTkjUJCwsjICBA5UcIIcTfL8GBx9HRkd27d2NnZ0dgYCCrVq2iffv25MuXD2tra7p06cLLlzE/BzFx4kQsLS2VP9FXUEIIIf5uP7069adPn9i7dy+HDh3i0qVL3Lx5U/nQZOrUqTl16hTZsmVTqxcWFkZY2Nel3QMCArCzs5NZbf8ImdX2b5FZbf+GuM5q++mVCwwMDKhbty4LFy7k6tWrvH37loULF2JlZcWbN2/o1k3z0vuGhoZYWFio/Pwt7t29Q+sWjcnikIa0Kc0oXjAvC+fNVnlqOa7CwsKYPX0KZYoVxC61JWmsTMibIzM9u3ZU7qAYm03r15LCVI8UpnpMmzw+3n0Q6nR1dahfPjcTu1fj8IKOvDs2htDzk1gyrGGC2suVKQ0z+9bi5NIuPNk1BL+T43h1eBQnlnSmc4Ni6CVT/1NNbWVGq5oF2DTJlUc7B+N/ahwvD43k0IIONK+W72cPUXwRERGB29bNDBnUn8rly2CTwhxzo2R0/K/NT7V7/txZ6teujr2tNWlSWlCmRBHWr139wzo+z5/TqX1bsmRMj7WlCXlzZWf8mFF8/Pjxp/qibYm+Vlvy5Mnp1KkTadOmpXbt2hw/fvyf2XwM4NLF89StXonQ0FDyFSiIvUMGzp85zdCBfbl08TzLV2+I8+KEHz9+pFbV8ly5dBHL5MkpVqIUhkZG3HS/zpqVy9i+bTO79h/R+NBotPfv3jFscD90dHTUtk0WCWduYsjacXF/yDc2JVwy0qlBMbxe+nLP8zXvfIOxtjKlqHMGCud2oE7ZXNTouUxla4ZJParTtIoLn8MjuHbvOedueJI2lQXFnDNQ0sWRasWz4zp8wz+72VtiCQwMpFWLpona5s7t22jVoimRkZEUL1GKlNYpOXn8GB3/a8Pt27c0rpz9+PEjypcuzvt378iRMxfFipfg+tWrTJowlhPHj7HnwOF476SaVH7ZIqHlyinWFIqIiMDPz++fCDyfP3+mY9uWhIaGMm7SNLp07wV8WRm3VhV2um1lQ6WqamupxWTV8qVcuXSRfPkL4Lb7oHIJnYiICIYM6MPSRfMZNqgfew4ej7GNoQP7EhIcTKMmzdm04d/Yc0UbPodHsG7/Na7de87Ve8/JYp+KpcMTdrUDcOCcBwfOTVFbuia1lRl75/5HqXyOtKtTiEVbzyvzPviHMGLRAVbsvKxc7Rogv1N69s75j3rlnDl66RHLd15KcL8E6Ovr07RZC1zyFyBf/gI8fOBB5w7qmzzG1YcPH+jS8T8iIiJYu3ELtevUA+DN69dUKleKubNmULVqdUqWLqNSr9N/bXn/7h2du3ZnyvRZAISHh9OyeWN279zB9CmTGDL8z9jmPsFDbbGdPT969AhQDMXFthTF78zby5MUpnp07aC+l/r39uzagZfnU3LlzqMMOqDYHG7KdMX+7vPnzIzza587q9ijo3P33irrtiVLlky57cH1q1dirH/86GE2b1xHnwGDcciYMc6v+6+yT2MV5+GykI+f+W/MZhZsOcfF296Effr8U6/t+eKDxvXS3vgGMX3NCQDK5M+kktdv1m6mrjqhEnQArt57zrQvdaIXARWqvDw94zxcZmpqypLlq+jctTuFixTFyMjop1571YplBAQEUL1mLWXQAUhtY8OYCYrN4ObMVt0V98rlS1w4f5ZUqVMzdsJkZbqenh6z5ixAX1+fRQvmqixK/DtLcOAZPnw4/fv35/Hjx2p5Pj4+dOzYEYBatWrFuhrs3+LwgX0A1KpbTy0vj0s+MmR05N7d23h7ecapPUOD2C+brVJo3sArJCSEvj27kjW7E917yQoSf7LwcMW9wU+f474D6q0vK2vbWv89907/Fgf3K74n6mhYkbpK1eoYGRlx4thRlfs20XWqVquhNpyW2saGYsVL4uvry/mzZ35hzxNPggNPUFAQ06ZNI3PmzGTLlo26devStGlTSpYsScaMGbl06RKZM2dm1qxZidjd39vtWzcByJNH87490fv53Ll9S2P+98qWrwjAwrkzCfD/uhlZREQEE8eNAqBFK81nbJPHj8bz6RNmzJ7/zwT+v1Fyc2N6NisJwP5z9+NcL2O6FAC8/hD4S/olEu72LcUeSXld1CeAGBgYkCNnLj5+/Mijhw+U6bd+UAcgj4viu+V2HL9bklqC7/EMGzaMAgUKcPDgQW7cuMHp06eVU+gKFSpE7dq16dKli8pufH+758+9AUibTn0VWYC0aRVb3z7z9opTe42aNufo4YO4bd1EnhyZKFykGIZGRty4fo23b17TvVdf+g8aplbv1g13Fs6bTTPX1hQrUSqBRyOSQqb0KRnYuhy6ujqktjKjSG4HzE0NWeJ2gY0H3ePUhl4yXTrUU6wIvycOK2kL7QkICMD/y0lkjN8T6dJx7eoVvL29lDsaP3/2TJmnSfReYd5x/G5JagkOPNbW1rRo0YIWLeK3S+bvrGaVcpw9fUpj3oZ1q9mwTn2q47xFy5STBYKDggAwjmEiRXQQDgqK21losmTJWLx8Nent7JgzcxqHvgzlAeTJm49SZcqpLaceERFBr26dsLC0ZMz4yd83Kb44OL8DpfI5asxzrZ4f1+rqMwXbj93C2n1XNdRIPDYpzNRee/6ms4xecijOsxJHdqiEU0Ybnvq8Z+n2i7+im3+UqhXLceb0SY1569eu1jiFeeGSZT/cIyehor8jgBgnXJmafPmeCPz6PRH0pZ6JseY60W19W+d3Jltff6N8hcrY22dQSQsKDmL3DjcyOmaiSNHianUcM2VSS0ssfr6+uDatz/WrV5g4dSY169TDxNiEc2dPM7BvTxrXq8mSFWup16CRss6i+XO4fu0Kcxf+jxQpNd//EXD4ggdeL1X3uTczNqBuudw8fv6OczfUzxyfPP8122B/69xNL4yLDkJXVwc7m+TULp2TIe0qUKloVmr0XI73K98f1m9YwZk+LUoRGvaZ1iM3Kre4/pdVqFQZ+++2lg4ODmLndjccHTNRpJimv+vM2ureP0kCzzd69VPfkdPby5PdO9woUrQ485cs/2F9UzMz/Hx91fZAjxYcrJh9ZGZmHqf+DB3Yl7OnTzF+8nQ6dumuTK9Woxa2adNSoVRRhg/uT83addHX1+eZtxeTxo2iWImScZ6y/a+atkb9DNg+jRV1y+Xm3A0vOozbkgS9+ioyMgqvl77M2XgGr5e+bJzkyoy+tWjQf1WMdUrnz8SSYQ2JjIyi1YgNXLrzTIs9/n317a/+d+3l6cnO7W4UKVacxf9bobW+mJqZKf8/JCRE44PzwSFfvifMv35PmH2pFxKq+bsl5Mt3zrd1fmcSeBJR+vT2+Pn68sLnOTm/jM1+68ULxZbFdvYOannfi4iIYNuWjQDU0jD7xSVfARwyZMTz6RM8nz4hS9ZsnD55guDgYN6+fUutquVVynt7Kc7g165awcnjR8mVOy8Tp85Qa1f8fnaevENgcBiVimRFXy+ZykOk0fI7pWfL5JYY6Cej04RtcdolVWifhYUFlpaW+Pv788LnORYWOdTKvPBRfE/Yf/M9kd7Ojhvu15V53/Pxea5W53f200vmiK+ibwTeuHFdY/5Nd0V6zly5Y23r7Zs3fPr0CQALC0uNZaLT/fxUh18eetzn7OlTKj/RExq8vTw5e/oUt2+5x35A4rfhGxCCvl4yrMzVt2DPniE1O2a0wdzUkIFz9rJm76+9DyV+Tq7cimer3K9fU8v7/Pkzd+/cxsjIiMxZsirTc/+gDsCN64rvllxx+G75HUjgSUQVq1QDYNd2N7W8m+7X8Xz6BKccubB3yBBrW1YpUiinQbtfU39INCAggEcPPQCws1Oc5TRzbcWH4HCNPwOGDAdgyIjRfAgOZ/eBYwk6RqF9GdKmIL2NJf5BH3nnr/qwqH0aK/bMbod1clPGLj3MvE1nk6iXIq4qV1V8T+zYvk0tb/++PXz8+JEy5cqrPKgaXWf/vj0qiyuDYsWDc2dPY2VlpfF+1e9IAk8s7B0y8CE4PNb7OwA1atXBIUNGbt+6wYK5s5TpwcHB9O+juEfTtUdvtXp1qlWksEtOrl75urSJoaEh5StWBmDYoP68+maLiY8fP9K/VzdCQkIoXLQYaWxtE3p44hver3wxLjrol97f2Tf3P9w39qFADtWptJ0bFMMmhZla+Sz21qwa3QRdXV3W77+msu5aKitT9sxuS7rUlsxaf4oJy4/+sn7/bRwyZCDwY8Qvvb9To0pF8jnn4Mpl1SWLWrVph4WFBXt372Lnjq8nqW/fvGHEkEEA9OjZR6VOgYKFKFK0uKLM0EHK9PDwcHr37Mrnz5/p1KU7+vr6v+x4EpPc4/nGrGmTefjAI151XFu3pUixEoBiTadFy1ZRr0Zlhg3qx/Ztm7Gzd+DC2TO8evWSWnXr07RFS7U2nj59wjNvL7VJCeMmTePq5UvcuulOIZccFCxUBGNjY65fvcLLly+wSpGC6bMXJPyA/2H9XEuT1SF1vOqs3HWJcze/znab1a82LtkUz1WksFRMZ61SLBsnl3ZRlindXvXzcUyXEgdbK4wNVR/q7dmsJFN71eDmo5c8ef4eHR3F1YxLtnQkS6bL6etPGL7wgEqdeQPrkcU+FcGhn0hpaapxqZ/3/sEMnrtPLf1fMn3qZB54xP3hW1AEh2LFSyj/3btHV9y/DJX7vn8PwMED+yhbqpiyzPFT51TaePrkMd4a/q5TpEjBgsX/o2XzJrg2bUTJUmVIkTIFJ44dxc/Pj249e6ut0wawcOkyypcuzoJ5czh54jjZnZy4duUKT58+oXCRYvQdMEitzu9KAs83jh45GONzPDEpXrK0MvAAFC5SjCOnLjBp/GjOnj7JnVs3yZAxE9169aVT1x5xXpkaIKNjJk5duMbsGVM4cvgg58+eJioqinTp7fivYxd69h2gfHBMxE/FItlifI4nJqeuPVEJPE4ZbSiUy16lTCorM1JZqV+5xGbUooNULpaNfNnTU6FwVowN9fgQEMrRyw/ZfPgG6/dfV3uOJ/mX+z2mxgYanzsC8Hrp+88HniOHDsb4HE9MSpYqrRJ47t+7x5VLqs9EvX/3jvfvEjbFvnbd+hw4coKpk8Zz+dJFPn36RHanHHTo1IXmMcxIzZw5C2cvXGXcmJEcOXyQ3Ts9sLOzZ+DgYfQbOPiPWZkaEmEjuMQSEBCApaWlbAT3j5CN4P4tshHcv0FrG8EJIYQQ8SGBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFZJ4BFCCKFVEniEEEJolQQeIYQQWiWBRwghhFbpJXUHvmegp4uBnsTDv93bExOSugtCi1IV6ZHUXRBaEBXxKU7l5BteCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYHni+vXrjJ96iSaNqpPVkc7zAx1MTNM+Ntz7Mhh6teugUO61CQ3NcDe1ppa1Sqza+d2tbIhISHs3rmDLh3b4ZIrOyktjEltZUaRAnmZOH4MQUFBGl/Dy9OTBnVqkiq5KQ7pUtO3V3c+fvyosezFC+cxN0rGsqWLE3xMf4OQkBB279pBl47/4ZLbCWtLE2xSmFO0oAuTxo+N8b3WZO3qlZgbJYv1Z/3a1Wp1fZ4/p2e3zjhlzkBKC2OyZExPx//a4Pn0qcbX+vjxI316dsMhXWpsUpjTqH5tvL28NJb19/cnk0Na2rg2i/Ox/A2yZbRhxfhWPDk0Hr+LM7m/dzQzBzYkZXJTtbItahYm9Pq8GH9WT2oTr9euXjo3S8e4cnnzEJ4dm0TApdl4HZ3I9rmdqVoyV4z1dHV16NykNGfXDeDduem8OjWVw8t6Ubtcnhjr1K/owvVtw/C7OJNr24ZSt0LeGMvOGdqEl6emkDqFebyO51fTiYqKikrqTgAEBARgaWnJi7d+WFhYaP31mzSoy57dO9XSg8Ii493W/DmzGNi/Dzo6OhQuUpR06e3wef6MixfOExUVRb+Bgxk1Zryy/Mrl/6Nb5w4AZMvuRI6cuQgMCODihXMEBgaSNVt2Dhw5QerUqZV1IiIiKJTPmQce96lQsTJv3rzmhvt12v7XgTnzF6n0JzIykpJFC6Krq8vJsxfR1U36842k+rVbufx/dO/SEYh+r3N+ea/Pf32vDx8n1TfvdUzOnT3DqhXLNOYFBPizZ5fi9+nm3YdkdHRU5t29c5tqlcvz/t07HBwykMfFhadPnnDr5g0sLCw4cOQEuZ1Vv3h6dO3EimVLyeuSD2tra44eOUy27E5cuOJOsmTJVMoO6NuLNatWcPXGXdKmSxev9+dXSVWkxy9tv3TBrGyb3RFTY0PuP3nF/aevyJHJlqwZbHj+ypcyrabj88ZPWb5FzcIsHePKDY/n3PR4rtbe5dueLN1yJs6vv35qO2qXy8Pdx6949uoDQSFhONimoJBzRgCmLDvIyHm7Vero6uqweUYHqpfOTWDwRy7ceIqurg5F8mTE1NiQcYv2MX7xPpU6RfM4cmR5L975BXPB/QlFXRxJaWlK+bYzuXBD9aTFxcmO02v6M3C6G/M3nIjzsfyMqIhPhN1air+//w+/xyXwfDFj2mSCg4PJn78g+QsUJEfWjISFhcU78Lx9+5bsmeyJjIxk175DlCxVWpl35vQpalevzKdPn1S+jNatWcWF8+fo2r0X2Z2clOVfvXxJ/To1uOF+nYaNm7Ji9Tplntu2LbRs1pgx4yfSp99AIiMjqVuzGidPHOP+Iy/S2Noqyy5dvJA+Pbtx9ORZChUuktC3KFEl1a/dujWruHjhPF269yR7dtX3ukHdmor3ulETln/zXifE/5YspHePbhQpWpzDx08p06Oioiha0IU7t2/h2qoNc+YvQk9PD4BFC+bRv09PsjvlUAkor16+xClLBspVqMjW7bvR0dFh6qQJjBk1nDXrN1GnXgNl+3du36JEkQKMGD2O3n37/9QxJKZfGXiMjfS5u3s0aawtGL94H+MWff2yntCrDr1bVeDwuXvU6jpfmR4deDR9uSdEnmzpefbKlw/+wSrpBXM5sHdRd0yNDSjUeBJ3Hr1Q5vV0LcekPvXw9HlHtU7zePr8HQBZM9iwf3F30qZOTplW07l482tAcZvTiTIFs5K33li8X/qSIV1K3N2GcfSCB/V7qp5wnljVFxMjA4o2m0xERPxPoBMiroEn6U99fxN9+g1k+MgxVKtRE5s0aRLczpVLFwkLC6N0mXIqQQegRMlSVKhYmaioKK5du6JMb+7airkLFqsEHYA0trbMmD0PgF073Pj06ZMy7+YNd0XdFq0A0NXVpXnLVoSHh3Pv7h1luffv3zN21HBatGz12wSdpNTctRVz5i9SCTqgeK+nz5oLwK6d21Xe64TYuH49AE2bNVdJP3/uLHdu38IqRQqmTJ+lDDoAnbp0o3CRYty/d5f9+/Yo0+/euU14eDhNm7VAR0cHANdWiqGgmzduqLTfr3dPMjpmoluPXj/V/z9J7XJ5SWNtgcfTV4xfvF8lb8S8XXj6vKNiMSdyZ/11V383PJ6rBR2Ay7e92HroGrq6upQumEUlr32DkgCMmrdHGXQAHni+VgbPPq0qqNTJky09p68+wvulLwCePu85deURebKpHptrrSIUds5I78mbtRZ04kMCTyIzNDSMU7kUKVLGqVz0kEtYWBgf3r9Xpvv5Kn7xkltZKdOskiv+39fPV5k2avgQIiMjGT1uUpxe718W03sdX55Pn3LxwjkMDAyo26CRSp779WsAuLjkw8zMTK1uqdJlANi7e5cyzc9P/bOO/n+/bz7rLZs2cOb0SabNmI2+vn6C+/+nyedkB8CZa4/VrqTDwyM57/4EgBplcmu9bwCfwyMA+PQ5QplmYWZEJvtUAJy6+lCtzskrDwCoUNQJA/2vJyfJzU3wCwxRKesXGEJyCxOVtsf2qMXGfZc5e+1x4h1IIpLAk8jyFyxE8uTJOXniGKdPnVTJO3P6FEcOHyRz5iwUL1EyTu09far4o9HX18cqRQplup29PQAPHz5Qpj144KHIs1PkXbt6hVUrljF0xGiV+0NCM88Y3uv42rRBMUxXuWo1rL4JFgDBwYqz4uTfpUdLkVJxQnL71k1lWvovn+ejh1+/oB5++ayj84KCghg6eAC16tSlXIWKCe77n8jEWHGy5xcQojE/+krEOWt6tTwXJzsm9KrD3KFNGNapGiXyZ07UvuXMnJYGlfLx6XM4Ry/cV6abGn89QfXV0O8Pfoo+mxgbkMXh69/us1cfyGyv+recxSE1z15+PQEZ2aUGxob6DJm1I7EOI9HpxV5ExIelpSXzF/+Pti2bU61SOYoULUbadOl54fOcC+fPUaRYcZYuW4WBgUGc2ls4bzYAFStVUbmaqlS5KqOGD2Xk0MEsXLqcN29eM3/uLNKmS0du5zxERUXRt1d3cuTMRYdOXX7Jsf5tFsybA0CFSpXjfOWqycYvgadJsxZqedbW1gB4e3trrOvl+fRL/tcZa8558pLG1pb5c2ZRqnQZrK1TMWLoIHR0dKhUuQoAkyaMxd/Pj0lTZiS433+qd76KmYj2aTWfLDikU7zndrbq+dVL56Z66a9XQkM7VuPUlYe4DlzOmw+B8e5LtVK5qFM+L/p6ybBLY0WRPI58Do+gy9gNKsNpH/yDCQ+PQE8vGfa2KXjg+VqlnQzpvo6I2NumUN4bOnj2Lj1alKNN3WJsPXSNxlULkDe7HbNWHwUUga5Dw5IMn7OLl2/9491/bZHA8wvUrlMPt137aNm8MefPnVWmW1hYUL5CxTjPNDq4fx+rVixHX1+f4aPGqOTlds5DqzbtWLn8fzjaKe5J6ejosGLNeoyMjFi9cjmXL13k4NGTKrOeQkNDMTY2ToSj/LscPLCP1Su/vNcjx8ReIQZXLl/i0cMHWKVIQZWq1dXyi5csBcC1K5e5f+8u2Z1yKPNCQkJw27YFgKDAr196RkZGjJswmfZtW1E4/9fZbv916ESu3M48eODBgrmzGTRkuPJKGBSftZGRkfK+0N/qzLVHDKQyVUrkJGVyU977fb3XkjaVJeULZwPA3OTrycSrdwGMXbiXPSdu8tTnPcaG+hTI5cD4nnUoVSALbnM6UarlNCIj4zcJJnfWdLjW+novNST0E/2mbmX9nksq5cI+hXP1rjeFnTPiWqsww+fsUslvVbuo8v/NTb/2e9ryQ9Sr4MKCEc1YMEIxXd775QemrTgEwMxBDXno/YZ5G44r6+jpKQa2wsN/n3s9MtT2C8yZOZ2aVStSvEQpLly9wesPgVy4eoNSZcoxbvRImjWqH2sbHvfv818bV6Kiohg3cYra9FqAuQsWs3LtBv7r0InuPXtz/PR5GjRsjJ+fHyOHD6FJsxYUL1GSyMhIRo0YSrrUVqRKbkqOrBnZtnXzrzj0P5KHx33at2n5w/c6rqKH2erVb6jxqjZr1mzUrF2HyMhIGtevw8njxwgMDOTWzRs0qFNTeW/p+ynvjZs25/Dx03Tt0Yv/OnRizfpNyokn/Xv3wM7Onp59+gGwdfNGcmZ1JLWVGXZpUjJm5HAiI3+fL53EduT8Pa7d9cbc1Iid87pQIKcDpsYGFHbOyI55XdD7cuL1bRA5cv4eE5bs5+YDHwKDP/LmQyD7Tt2mRIspPPB8Tf6cDjSolC/efZn8v4MYu3QjeeFe5G8wntW7LrBgRDO2zOyAvp7qtPfoYNGzRXl6uZbHJqU5tqksGdCuMv81KMHnL/eEvu33W98gijSdxPC5u1i27SzDZu+gaNPJvPcLpnGVApTMn4U+k7cQHh6JQ9qU7F7QFb8Ls/C7MIvdC7pib6t5iFfbZDp1DFJaGCdoOvWpkyeoVqkceV3ycercJZUvkIiICEoWLcjNG+647dxLpSpVNbbxwseHCmVL4O3lRfeevZk4ZXq8+tC3V3fWr12N+20PbNKkYe6sGQwe2I+u3XtSqnRZli1dzJHDBzlz4QrOefLGq+3E8pv82vHCx4eKZUvi7e1Ft569mTh5WoLbCg8PJ6ujHW/fvOHw8dMUKVpMYzlfX1+aNqzH2TOnVNLNzc0ZMnwUgwf0JY2tLQ+fqj9f8r2d27fRomkjtmzfRZWq1bl+7SqlixemQsVKtG3fkTOnTzF/ziymTJ9F567dE3xsP+tXP8djb2uF25zO5MycViX91bsAFm48yehuNblw4wllW8c+FNmxUSlmDW7Eml0X6DBy7U/3bfqABnRpWoZBM9yYveaYSl6fVhUY3a0met8Fpf9tPUPe7OkpkCsDNTrPU7k/pImZiSE3tg/nwo0nNB+wHB0dHc6tH0Da1MkZOmsHUSimlj975UuJ5lN++phiEtfp1DLUlsg2rl8DQM3addTOWpMlS0atOnW5ecOds2dOaQw8Hz58oFb1ynh7eeHaqjUT4vlFePvWTf63ZBHjJkxWTgufPWs6JUuXYfK0mQCUKlOWrBnTM3vmdJatXJOQw/wrfPjwgdo1quDt7UWLlq2ZMGnqT7V39Mgh3r55Q8aMjjEGHQArKyv2Hz7GoYP7OX3qJAH+/mR0zETjJs3wuH8PAKdvhuBiEhoaypCB/alavYZyWG/urBmYmZmxat0mzM3NqVGzNjeuX2fWjKlJGnh+Ne+XvhRuMonaZfNQJE9GjIwMuPf4JRv3XaZ2ecUV7N3HL+PU1iPvNwCksbZMlL6t33OJLk3LUKOMs1rgmbHqCDuP36BuBRccbFMQEBTK/jN3OHP1EY8OjAXgXhz6PaRDVSzNjRk43Q2AcoWzkTe7Hf+NWMO63RcB0AGWjnGlbOFsHL/okSjHllASeBKZz3MfACwsNP/SRqf7+vqq5QUFBVGvVjXu37tLrTr1mLdwabzH5/v06k6WrNno3E1xhhkQEMCrly9p0vTr8yTm5uZkzZad+/fuxqvtv0lQUBD1a1f/8l7XZd7CJT99L2TTesUwW+OmzWMpqbgfV7lKNSpXqaaSvnrlcgBKlioTaxvTJk/kzZvX7Jl6RJn24IEHWbNlx9z86xIp+QsW5MzpkwQEBPwWowm/SkREJG5HruN25LpKehFnxYPap6+oT1vWxOrL1OSQ0LBE6de7L/ecrJOrT58HeOz9lmnLD6mk2aWxIp2NFY+83/AilkkCWTPY0LVZGcYt2sfz136AYvkggKt3vk5SufLl/50c00jg+dtEX2Vcv3ZVY/61q4oHRx0yZFBJDwsLo3H9Oly5fIkKFSuzcs16taVQYrNpwzrOnTnNngNHVB5MBMWN628FBwf/s5MMwsLCaNKg7pf3uhIrVsf/vf5eUFAQe/cobhA3bhZ74NEkJCSE1SuXY2BgQLMWLX9Y9snjx8yeOY3efQeoLMcT3Y7Kv79M4f7bJxloYpPSnLoV8vLON4gdx27EXgGoUz4vANfvP0uUPpT8MkX7yTez2mLTuYni4fPl287GUhJmDGyI14sPyplt3zIx+nqf0fTL/8d3wsSvIJMLEmjRgnm45HZi5LDBKuk1atUGFEFg/949Knl7du1k88b16OrqUrNWXWV6REQErV2bcfLEMYqVKMn6zdviPN06WmBgIEMHD6Beg0aUKVtOmW5hYUG69OnZt3c3gV9mSt1wv47H/Xs45cgZr9f4G0RERNCm5Zf3unhJ1m2K/b1evHA++ZxzMHLYkBjL7NrhRkhICAULFyFz5iwxlgPFs1cBAQEqaR8+fKBViyY8e+ZNvwGDSZde/ZmTbw3o14s0aWzp03+gSrqTUw7u37vLDXfFWX9gYCD79+7Bzs5e5Srob5Mjky2GBqonW+lSJ2fLzI5YmBkzaOZ2PoZ9Vub1a1tJbfFQPT1dhnSoSv1K+QgJ/cSanRdU8tOmssTdbRjubsNU0q2tzGhTtxjGRuoP7ZYrnJ3xveoAsGaXansmRgbKK5NvtatfnO7Ny+Lx9BXzN5xUy/9W3Qp5KV8kO/2mblU+qApfhxWbVC2gTGtSraBKXlKK1xVPs2bN2LBhA2PGjGH48OE/LHvp0iUKFy5M6tSp8fHxUTsD/90c2LeXyRPHKf8dvWRK2ZJfpzUOHDyMKtUUY+nv37/j4QMPXr16pdJOzVp1qFu/Idu3baFhvVrky18AhwwZ8fJ8qrzaGTlmHFmzZVPWWbRgHru/rFqdMqU1vXt01djH8ZOmKp8D+d7EcaMJDAjQeHO8T7+B9O3VnSL58+Cc14VTJ46hq6tLr99oLS9tWbxwPrt37gAgpXXKOL3X799Ff9Yx/8FGz2ZrquHZne9t2biBmdOnkC9/QdKmTUtAgD/nzp4hKCiI5q6tGDhk2A/r79+3h4P797F+8za1q9aeffqxedMGqlcuT6kyZbnp7s7z58+YPXdBrP36k/VqWZ5aZfPgfv8Zr94FkCqFGcXyZsLIUJ8JS/Yr73NEG9u9FkM7VOXaXW+ev/bF3NSIPNnSkzZ1ckI/fqLtsFVqQ1x6esnIllF9OS0TIwMWjGjG1P71uX7vGT6v/RQPftqnJrujovyctcfYcdRdpZ61lRnubsO58+gFj73f8jk8AhcnOxztUuHp847a3Rby6XN4jMdsbKTPpD712H38BofOqg6bn7j0gCu3PeneohzO2dITFRVFmULZuHjzKafiOOT4K8UrGri6urJhwwbWrVsXa+BZu1YxG6Rp06a/fdABePfuLZcvXVRL/zbt3bu3sbajo6PD6nUbWVOpMuvWrubOrZvcvOGOZfLkVK5SjU5dulHxy0N/0b5d9mS3hm0Tog0ZNlJj4Ll/7x4L589l2MjRGs+UO3TqQlBQIEsXL+TAvj1kzZad0WMnkOMfvOLx8/32vd4RY7mY3mtNXr18yckTx9HX16fed0vkaFKqTFlu3nTH/do1rly+iKmZGYWLFKVt+47Uql33h3XDwsIY1K8PFSpWomatOmr5uXI7s2GLG2NHjeDAvr3Y2KRh9LiJtG3fMU7H8qfaffwmNiktyJ01HUXzOuIbEMLhc3eZu+4EpzUsSTN+8T4KO2ckawYb8ma3Q0cHfN74sXTrGeauPcZDrzdxfu23voEMmbmdkgWykCOTLfmc7NHV1eHVO382H7jC/7ae1dgH34AQlmw5TYl8mSlTKCvJkuni6fOecYv2MWv1EYJDf7xe4IC2lUllZUb/aW4a8xv1Wcr0AQ2oUFSxLuH2I9fpM3lLnI/rV4rXdOqIiAjSpUvH69evuXTpEgULFtRYLjw8nHTp0vHmzRuuXLlC/vz5Y237d5tOLX6t32U6tdCOXz2dWvwefsnq1MmSJaNp06bA1ysaTQ4dOsSbN29wcnKKU9ARQgjx74j35IIWLRRj2Js2bSIiIkJjmXXr1qmUFUIIIaLFO/Dkz58fJycnXr9+zeHDh9Xyg4OD2blzJzo6OjRvnrBppUIIIf5eCZpO7erqCmgebnNzcyM4OJhSpUrh4ODwc70TQgjx10lQ4GnevDk6Ojrs2LFDub9ItOhgFNswW1hYGAEBASo/Qggh/n4JCjz29vaUKlWK4OBgduzYoUx//fo1R48excjIiIYNG/6wjYkTJ2Jpaan8sbOzS0hXhBBC/GESvHKBpuG2DRs2EBERQc2aNbG0/PECe4MHD8bf31/58+xZ4ixP8Tu4e/cOLZo2wiFdaqwtTSiUz5n5c2YlytL0kyaMxcxQFzNDXTauVx/qHD92lDL/Rz9nTp/S0LqIq4iICNy2bmbIoP5ULl8GmxTmmBslo+N/bRLUXnBwMBvWraFf7x6ULVmUlBbGmBslY8LY0THW8fX1ZeSwIdSsWokcWTKS2sqM1FZmFHTJzbAhA3n3Lu5LtIiY6erqUL+iCxN71+Xwsl68Ozed0OvzWDI6YZOnSubPQuj1eTH+nFzVV2O9KiVyMqprTfYu6sbLU1MIvT6Pg0t7/syhJZkEP9nZoEEDunXrxpEjR3jz5g2pU6dWBqHooPQjhoaGP7XL4+/q4oXz1KhSgdDQUAoULIS9QwbOnjnFwP59uHDhPKvXbUzwmlkPPDyYOmkCOjo6MT4H4+ycl+aurTTmvfDx4fixI5iYmJDXJf57jYivAgMDadWiaaK19/jRQzq0ax2vOi9f+DBj2mSsUqTAySknhYoUITAwkOvXrjJ7xjQ2b1zPoaOnyJAxY6L1819kbmLE2intEr3dx95vOef+WC09pjXdVkxoRXJzk0TvR1JIcOCxtLSkVq1abN68mQ0bNlC5cmWuXr2KtbU1VapUib2Bv9Dnz59p16oFoaGhTJoynW49ewOKBSRrV6/M9m1bWFelKi1ato5321FRUXTv2hHL5MkpVKgIe3bv1FiuZu061KxdR2Pe8CEDOX7sCDVr18XMTPNKuSJu9PX1adqsBS75C5AvfwEePvCgc4eEfzmZmZnTsnVb8ucvQL4CBTm4fx/jxoz8YZ106e04de4SefK6qGzB8fHjR3p06ciG9WsZNmQAazf8Hk+r/6k+h0ewbs9Frt315uodb7I4pGbpmNhPrmNzzv1xvPb72XHUHY8nr7l61xt9vWTsXdTtp/uQVH5qkdDoCQTr1q1TPrvTuHFj9PXVF8v7U3l5emJmqBunIZRdO7fj6fmU3M55lEEHwMzMjOmz5gIwZ1bsG1FpsnL5/zh7+hQTJ0/DMnnyeNePiopiy+aNQNzWE/sXeXl6xnm4zNTUlCXLV9G5a3cKFymKkZHRT722Y6ZMzF+0lLbtO5LXJR96cfgbsrS0xCVffrV9n4yMjBg5ZjwAp04c11T1n2dvmyLOw2UhHz/x3/A1LNhwkos3nxL2Keb1036lzqPXM2vNUU5ffUhQyMck6UNi+anAU6VKFaytrbl8+TKLFi0C4jbM9rc6uH8fAHXqqW9tndclHxkzOnL3zm28PD3j1e7rV68YPmQgZcqWj9NeL5qcOnmC58+eYZMmDWXLV0hQG+LPEX3ypx/PVc6F0IafCjz6+vo0adIEgHfv3pElSxYKFy6cKB37E926qdjvI29ezfdP8ny5r3L71s14tduvT09CQ0OZ9RMrDG/aoLikb9ioyU/vPSN+b58/f2bCOMWkhO83mhO/j8z2qRjTvRbzhjVldLeaVC6R45/ZM+mnl412dXVl3rx5gCyR8/yZN0CMe6mkS5cOAG9vL435muzfu4ft27YwdMQoMmf58T4vMfn48SM7tytWsG3a/N+9Iv2bden4HxEREfj5+eJ+/RovfHwoUrQ4YydMTuquiRgUzZuJonkzqaTdeuBD0/7/47F37Cvh/8l+OvAUKlTor1lpuErFspw5pXnjpXVrVrFuzSq19EVLlysnCwQFBQFgbKx55omJqemXcoFx6k9QUBC9e3YlS5as9Ok3MPYKMdi7Zxf+/v445chJnrwuCW7nb1K1YjnOnNb8Wa9fu5r1a1erpS9csixBE0O0Yf3a1SprJ5YsVYaFS5aRMmXKJOzV7+Hg0p6UKqD5pM21VhFcaxVRS28/Yg1rd6tvk5IYAoJCmbHyMDuOuvPoS4Bxzpae0d1qUtg5I3sWdKVwk0kEBP3Z93F+5PffKEeLKlaqjINDBpW0oKAgdm7fhqNjJooWL6FWxzFT5l/Wn1HDh/D82TP2HDjyU1PPN63/sklZ83/7ivRbFSpVxv67JZ2Cg4PYud0NR8dMFClWXK3Or/ysf5ZfsGLvllcvX3Lh/FlGDR9KkQJ5WLNhMxUqVk7i3iWtw+fu4vXivUqamYkhdSu4xDyl+dmvu+K44fGcGx7PVdJOXn5AuTYzOLi0JyXyZaZDo1JMW37ol/UhqUng+Ubf/oPU0rw8Pdm5fRtFi5dg8f9W/LC+mZkZvr6+hIaGaMwP+bK8kJlZ7FsQX7l8iSWLFtC0uavKVtbx9f79ew4fOoCuri6Nm8iirdH69le/glR81m4UKVY81s/6d5XG1pY69RqQL39BihTIQ6f2bblx5wGmpqaxV/5LTVuhvpixvW0K6lZwifeU5l8pMjKK6SsOUyJfZioWdfqrA89PTS4QqtLb2QPg8/y5xnwfHx8A7O1jXzz14IF9REZGcuf2LapULKvyc/jQAQCmTJpAlYplmT51UoztbNuyic+fP1OiVOkY7z2Jv4+9gwNFi5fg9atXXNGws674PUUPvaWx/rs3w5QrnkSU2zkPt27ewN39GpWrqs8munH9GqDYnjiubt5wjzHvgcd9HnjcVxse/NamDV+G2eTZnX9OypSKrbvjsmW7+D1YWRgDxLrt9Z9OrngSUXSw2eG2TS3vhvt1nj59Qo6cuXDIkCHWtoYOH0VQWKTGn+glcf63YjVBYZExDgs9ffKEixfOY2xsTO266s8Wib9XREQEF86dBSCjY6ZYSovfRZ3yeQFwv//3rF2piQSeWDhkyPDDL/dv1apdlwwZMnLr5g3mzZ6pTA8ODqZPT8XyFj169VGrV71yBVxyO3Hl8qXE6ziw8cuzO9Vr1v7h/udCwSFDBgI/RvzS+zs1qlQkn3OORPmst27eyJ3bt9TSP3z4QPcuHXn69Ak5c+XGJZ9sP/8975cfMHbp9kvv7+xb1B13t2EUyKk6tN6tWRnS2yRXK9+ufnG6Ny9HZGQkS7ec/mX9+h3IUNs3pk+dxAMPj3jVadWmHcW+zHbT19fnfyvXULNqRQYN6Mu2rZuxs3fg3NnTvHr5kjr1GmhcwPPp08d4e3kREqJ5UkJCbd6wHpBhNk2mT53MA4/78arz7WcN0LtHV9zdrwPg+14xa+rggX2ULVVMWeb4qXMqbTx98hhvby9CNXzWTRvV49WrVwC8evECgFUrl3H48EEA0qRJw4bNbsryhw8dpE3L5mTM6EiOXLkwMTbhxYsX3HC/RlBQEGnTpWPl2g3/zEOJMenXpiJZM9jEq87K7ec45/5E+e9Zgxvhkl2xdUuK5IqJGlVK5lRZSbp0q+kqbTjaWeOQNiXGRqqrR3RrXpaJvevifv8Znj7vMTTUJ1fmtGRMb01ERCR9p2zl+j31K55B7atQtUROAExNFLNc82ZPr9KHxn2X8urd77+3mQSebxw+dDDG53hiUrJUaZUvoyJFi3Hy3CXGjxnF6VMnuHXzBhkdM9Grdz+6dO+ptS+BK5cv8fDhA1KlTk35ipW08pp/kiOHDsb4HE9Mvv+s79+7p3bj/v27d7xP4HYEN93d1R4ufuHjw4sYJqW0atMOU1NTLpw/x4Xz5/D388PMzAynnLmoWq0GHTp1iXV7kn9BxWI5YnyOJyanrjxUCTxOjrYUclZd5TuVlTmprGKfofq92WuOUb5IdnJksiW7oy36erq8ehfA+j2XWLDhBFfvemus55jeWq0PFmbGKmkG+n/GV7pO1G/y9GdAQACWlpa8eOsnw0L/gN/k105oSaoiPZK6C0ILoiI+EXZrKf7+/j/8Hpd7PEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgitksAjhBBCqyTwCCGE0CoJPEIIIbRKAo8QQgit0kvqDkSLiooCIDAwIIl7IrQh+vMW/4aoiE9J3QWhBdGfc2x/379N4AkMDAQgm6N9EvdECCHEzwgMDMTS0jLGfJ2o3+TUMzIykhcvXmBubo6Ojk5Sd0drAgICsLOz49mzZ1hYWCR1d8QvJJ/1v+Nf/ayjoqIIDAwkbdq06OrGfCfnt7ni0dXVJX369EndjSRjYWHxT/2C/svks/53/Iuf9Y+udKLJ5AIhhBBaJYFHCCGEVkngSWKGhoaMHDkSQ0PDpO6K+MXks/53yGf9Y7/N5AIhhBD/BrniEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVkngEUIIoVUSeIQQQmiVBB4hhBBaJYFHCCGEVv0fs5ZGPPv7hxAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N -> Precision: 99.64%±0.05; Recall: 94.34%±0.74; F1-score: 96.92%±0.38\n",
      "V -> Precision: 89.59%±3.59; Recall: 95.39%±1.51; F1-score: 92.35%±1.88\n",
      "S -> Precision: 43.04%±3.49; Recall: 92.35%±1.32; F1-score: 58.62%±3.16\n",
      "Balanced accuracy: 94.03% ± 0.64\n"
     ]
    }
   ],
   "source": [
    "results = pkl.load(open('results/experiment_1.pkl', 'rb'))\n",
    "#results = pkl.load(open('results/experiment_2.pkl', 'rb'))\n",
    "#results = pkl.load(open('results/experiment_3.pkl', 'rb'))\n",
    "\n",
    "conf_matrices = []\n",
    "for y_test, y_pred in results:\n",
    "    conf_mat = confusion_matrix(y_test, y_pred)\n",
    "    conf_matrices.append(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1))\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "conf_matrices = np.array(conf_matrices)\n",
    "mean_conf_matrix = np.mean(conf_matrices, axis=0)\n",
    "std_conf_matrix = np.std(conf_matrices, axis=0)\n",
    "\n",
    "\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "cax = ax.matshow(mean_conf_matrix, cmap=plt.cm.Blues)\n",
    "\n",
    "color_threshold = mean_conf_matrix.max() / 2\n",
    "\n",
    "for (i, j), val in np.ndenumerate(mean_conf_matrix):\n",
    "    color = \"white\" if mean_conf_matrix[i, j] > color_threshold else \"black\"\n",
    "    ax.text(j, i, f\"{val:.2%}\\n±{std_conf_matrix[i, j] * 100:.2f}\", ha='center', va='center', color=color, fontsize=15)\n",
    "\n",
    "labels = [num_to_label[i] for i in range(len(allowed_labels))]\n",
    "ax.set_xticklabels([''] + labels, fontsize=15)\n",
    "ax.set_yticklabels([''] + labels, fontsize=15)\n",
    "\n",
    "#plt.savefig(\"figs/cm_result_1.pdf\")\n",
    "plt.show()\n",
    "\n",
    "print_results([classification_report(y_true, y_pred, output_dict=True) for y_true, y_pred in results])\n",
    "balanced_accuracies = [balanced_accuracy_score(y_true, y_pred) for y_true, y_pred in results]\n",
    "print(f\"Balanced accuracy: {np.mean(balanced_accuracies) * 100:.02f}% ± {(np.std(balanced_accuracies) * 100):.02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c8075f19-5033-476f-87a1-57df45791211",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5309/908863573.py:30: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([''] + labels, fontsize=15)\n",
      "/tmp/ipykernel_5309/908863573.py:31: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([''] + labels, fontsize=15)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N -> Precision: 99.64%±0.05; Recall: 94.34%±0.74; F1-score: 96.92%±0.38\n",
      "A -> Precision: 66.36%±2.88; Recall: 97.00%±0.39; F1-score: 78.76%±2.01\n",
      "Balanced accuracy: 95.67% ± 0.37\n"
     ]
    }
   ],
   "source": [
    "results = pkl.load(open('results/experiment_1.pkl', 'rb'))\n",
    "#results = pkl.load(open('results/experiment_2.pkl', 'rb'))\n",
    "#results = pkl.load(open('results/experiment_3.pkl', 'rb'))\n",
    "\n",
    "conf_matrices = []\n",
    "filtered_results = []\n",
    "for y_test, y_pred in results:\n",
    "    y_pred = np.where(y_pred > 0, 1, y_pred)\n",
    "    y_test = np.where(y_test > 0, 1, y_test)\n",
    "    conf_mat = confusion_matrix(y_test, y_pred)\n",
    "    conf_matrices.append(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1))\n",
    "    filtered_results.append((y_test, y_pred))\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "conf_matrices = np.array(conf_matrices)\n",
    "mean_conf_matrix = np.mean(conf_matrices, axis=0)\n",
    "std_conf_matrix = np.std(conf_matrices, axis=0)\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "cax = ax.matshow(mean_conf_matrix, cmap=plt.cm.Blues)\n",
    "\n",
    "color_threshold = mean_conf_matrix.max() / 2\n",
    "\n",
    "for (i, j), val in np.ndenumerate(mean_conf_matrix):\n",
    "    color = \"white\" if mean_conf_matrix[i, j] > color_threshold else \"black\"\n",
    "    ax.text(j, i, f\"{val:.2%}\\n±{std_conf_matrix[i, j] * 100:.2f}\", ha='center', va='center', color=color, fontsize=15)\n",
    "\n",
    "labels = ['N', 'A']\n",
    "ax.set_xticklabels([''] + labels, fontsize=15)\n",
    "ax.set_yticklabels([''] + labels, fontsize=15)\n",
    "\n",
    "#plt.savefig(\"figs/cm_result_3_abnormal.pdf\")\n",
    "plt.show()\n",
    "\n",
    "print_results([classification_report(y_true, y_pred, output_dict=True) for y_true, y_pred in filtered_results], num_to_label={0.: 'N', 1.: 'A'}, allowed_labels=(0, 1))\n",
    "balanced_accuracies = [balanced_accuracy_score(y_true, y_pred) for y_true, y_pred in filtered_results]\n",
    "print(f\"Balanced accuracy: {np.mean(balanced_accuracies) * 100:.02f}% ± {(np.std(balanced_accuracies) * 100):.02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c46f9535-cb6a-4e3a-99f5-9277d8c210aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5309/155468662.py:14: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([''] + labels, fontsize=15)\n",
      "/tmp/ipykernel_5309/155468662.py:15: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([''] + labels, fontsize=15)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N -> Precision: 91.71%; Recall: 99.49%; F1-score: 95.44%\n",
      "S -> Precision: 99.44%; Recall: 91.01%; F1-score: 95.04%\n",
      "Balanced accuracy: 95.25%\n"
     ]
    }
   ],
   "source": [
    "cm = np.array([[43962, 147 + 79], [329 + 124, 1369 + 123 + 13 + 3079]])\n",
    "cm = cm / np.expand_dims(cm.sum(axis=1), axis=1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
    "\n",
    "color_threshold = cm.max() / 2\n",
    "\n",
    "for (i, j), val in np.ndenumerate(cm):\n",
    "    color = \"white\" if cm[i, j] > color_threshold else \"black\"\n",
    "    ax.text(j, i, f\"{val:.2%}\", ha='center', va='center', color=color, fontsize=15)\n",
    "\n",
    "labels = ['N', 'A']\n",
    "ax.set_xticklabels([''] + labels, fontsize=15)\n",
    "ax.set_yticklabels([''] + labels, fontsize=15)\n",
    "\n",
    "#plt.savefig(\"figs/cm_result_original_abnormal.pdf\")\n",
    "plt.show()\n",
    "\n",
    "class_recall = []\n",
    "class_precision = []\n",
    "class_f1_score = []\n",
    "num_classes = cm.shape[0]\n",
    "\n",
    "for i in range(num_classes):\n",
    "    TP = cm[i, i]\n",
    "    FP = np.sum(cm[:, i]) - TP\n",
    "    FN = np.sum(cm[i, :]) - TP\n",
    "    precision_i = TP / (TP + FP) if (TP + FP) != 0 else 0\n",
    "    recall_i = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
    "    f1_score_i = 2 * precision_i * recall_i / (precision_i + recall_i) if (precision_i + recall_i) != 0 else 0\n",
    "    class_recall.append(recall_i)\n",
    "    class_precision.append(precision_i)\n",
    "    class_f1_score.append(f1_score_i)\n",
    "\n",
    "\n",
    "for label in [0, 1]:\n",
    "    print(f\"{num_to_label[label]} -> Precision: {np.mean(class_precision[label]):.02%}; Recall: {np.mean(class_recall[label]):.02%}; F1-score: {np.mean(class_f1_score[label]):.02%}\")\n",
    "print(f\"Balanced accuracy: {np.mean(class_recall):.02%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aea8ad4-6e8c-471e-b92e-6d30bff6f98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_recall = []\n",
    "num_classes = conf_matrix.shape[0]\n",
    "\n",
    "for i in range(num_classes):\n",
    "    TP = conf_matrix[i, i]\n",
    "    FN = np.sum(conf_matrix[i, :]) - TP\n",
    "    recall_i = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
    "    class_recall.append(recall_i)\n",
    "\n",
    "balanced_accuracy = np.mean(class_recall)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
